olxronda660143
/
adminakademia
1
0
Code Issues Packages Projects Wiki

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

Ronda Tiller 2025-04-07 04:03:56 +08:00
commit 96c37e1b5d
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 excited to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://palsyworld.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and responsibly scale your generative [AI](http://wj008.net:10080) concepts on AWS.<br>
<br>In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the designs as well.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language model (LLM) [established](https://haloentertainmentnetwork.com) by DeepSeek [AI](https://satyoptimum.com) that utilizes reinforcement learning to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing function is its reinforcement knowing (RL) step, which was utilized to refine the model's responses beyond the standard pre-training and fine-tuning procedure. By including RL, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, suggesting it's equipped to break down complex questions and factor through them in a detailed manner. This guided thinking process enables the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually caught the industry's attention as a [flexible text-generation](https://dreamcorpsllc.com) design that can be integrated into numerous workflows such as agents, sensible reasoning and data interpretation tasks.<br>
<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, enabling effective inference by routing questions to the most appropriate expert "clusters." This approach permits the design 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 reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled designs bring the [reasoning abilities](http://78.108.145.233000) 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 describes a procedure of training smaller sized, more efficient designs to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher design.<br>
<br>You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and evaluate models against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:ArnulfoHyett8) Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](https://radiothamkin.com) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 design, 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, 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](http://112.74.93.6622234) a limitation boost, create a [limit increase](http://lty.co.kr) demand and connect to your account group.<br>
<br>Because you will be deploying this model with [Amazon Bedrock](https://supremecarelink.com) Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to use [Amazon Bedrock](https://git.paaschburg.info) Guardrails. For guidelines, see Establish consents to use guardrails for [pediascape.science](https://pediascape.science/wiki/User:BelenTalbot935) material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails [enables](http://gitlab.mints-id.com) you to introduce safeguards, prevent damaging content, and examine models against crucial safety criteria. You can execute safety measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to [apply guardrails](http://gitlab.ileadgame.net) to assess user inputs and model responses deployed on [Amazon Bedrock](http://gitlab.mints-id.com) Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing 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 steps: First, the system gets 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](http://test.9e-chain.com) the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is [stepped](https://www.elcel.org) in by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas demonstrate reasoning utilizing this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To [gain access](http://yijichain.com) to DeepSeek-R1 in Amazon Bedrock, complete the following steps:<br>
<br>1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 model.<br>
<br>The design detail page offers important details about the model's abilities, pricing structure, and implementation standards. You can discover detailed use guidelines, [including](https://rapid.tube) sample API calls and code bits for integration. The model supports different text generation jobs, including material development, code generation, and question answering, using its reinforcement finding out optimization and CoT thinking abilities.
The page likewise consists of implementation choices and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.<br>
<br>You will be triggered to set up the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For [Endpoint](https://jobs.alibeyk.com) name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of instances, go into a number of circumstances (between 1-100).
6. For example type, select your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For many utilize cases, the default settings will work well. However, for [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/chantedarbon) production deployments, you may wish to review these settings to align with your company's security and compliance [requirements](https://novashop6.com).
7. [Choose Deploy](https://sagemedicalstaffing.com) to begin using the model.<br>
<br>When the deployment 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 interface where you can experiment with various triggers and adjust design criteria like temperature and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For instance, content for reasoning.<br>
<br>This is an outstanding way to check out the model's reasoning and text generation abilities before integrating it into your applications. The play area supplies instant feedback, helping you comprehend how the design reacts to various inputs and letting you fine-tune your prompts for optimum outcomes.<br>
<br>You can rapidly [evaluate](http://api.cenhuy.com3000) the design in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the [endpoint ARN](https://git.synz.io).<br>
<br>Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example shows how to perform reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce 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 created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends a demand to create text based upon a user timely.<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 services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.<br>
<br>[Deploying](https://paknoukri.com) DeepSeek-R1 design through SageMaker JumpStart offers two practical approaches: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker [Python SDK](https://ec2-13-237-50-115.ap-southeast-2.compute.amazonaws.com). Let's explore both methods to assist you pick the method that finest matches your requirements.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:<br>
<br>1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be prompted to develop a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.<br>
<br>The design internet browser displays available designs, with details like the supplier name and design abilities.<br>
<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card shows essential details, consisting of:<br>
<br>- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if suitable), indicating that this model can be registered with Amazon Bedrock, permitting you to [utilize Amazon](http://test.9e-chain.com) Bedrock APIs to invoke the model<br>
<br>5. Choose the model card to view the design details page.<br>
<br>The design details page includes the following details:<br>
<br>- The design name and company details.
Deploy button to release the model.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of crucial details, such as:<br>
<br>- Model description.
- License details.
- Technical specifications.
- Usage guidelines<br>
<br>Before you deploy the design, it's suggested to review the model details and license terms to verify compatibility with your use case.<br>
<br>6. Choose Deploy to proceed with release.<br>
<br>7. For Endpoint name, utilize the instantly produced name or create a customized one.
8. For example type ¸ select an [instance type](https://my-estro.it) (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the variety of instances (default: 1).
Selecting suitable circumstances types and counts is crucial for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for precision. For this model, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to deploy the model.<br>
<br>The implementation procedure can take a number of minutes to finish.<br>
<br>When release is complete, your endpoint status will change to InService. At this moment, the model is all set to accept reasoning demands through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, you can conjure up the model using a SageMaker runtime customer and incorporate it with your [applications](https://git.desearch.cc).<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary [AWS consents](https://wiki.communitydata.science) 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 releasing the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.<br>
<br>You can run additional demands against the predictor:<br>
<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:<br>
<br>Clean up<br>
<br>To avoid unwanted charges, finish the steps in this section to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace deployment<br>
<br>If you [released](https://merimnagloballimited.com) the model using Amazon Bedrock Marketplace, total the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
2. In the Managed deployments area, locate the endpoint you want 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 release: 1. [Endpoint](https://dlya-nas.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 expenses if you leave it running. Use the following code to erase the endpoint if you desire to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br>
<br>Conclusion<br>
<br>In this post, we explored how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker [JumpStart](https://actv.1tv.hk) in SageMaker Studio or Amazon Bedrock [Marketplace](http://git.thinkpbx.com) now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, 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 assists emerging generative [AI](http://www.xn--1-2n1f41hm3fn0i3wcd3gi8ldhk.com) business build innovative options utilizing AWS services and sped up compute. Currently, he is focused on establishing methods for fine-tuning and optimizing the reasoning efficiency of big language designs. In his complimentary time, Vivek delights in hiking, seeing movies, and trying various foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://work.diqian.com:3000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://c3tservices.ca) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://aceme.ink) with the Third-Party Model Science team at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and tactical partnerships for [Amazon SageMaker](https://shareru.jp) JumpStart, SageMaker's [artificial intelligence](https://www.so-open.com) and generative [AI](http://fcgit.scitech.co.kr) hub. She is enthusiastic about developing services that help consumers accelerate their [AI](https://sistemagent.com:8081) journey and unlock organization worth.<br>