From 8594916d62c9980a561952bb64fc7c15b5c1a51d Mon Sep 17 00:00:00 2001 From: olaelizondo900 Date: Fri, 28 Feb 2025 04:11:06 +0300 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..9ee803c --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://git.sofit-technologies.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion parameters to construct, experiment, and [responsibly scale](https://projobs.dk) your generative [AI](https://www.frigorista.org) concepts on AWS.
+
In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the designs too.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://surreycreepcatchers.ca) that utilizes reinforcement finding out to enhance reasoning [abilities](http://gogs.oxusmedia.com) through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying feature is its reinforcement learning (RL) action, which was used to improve the design's responses beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust better to user [feedback](http://gamebizdev.ru) and objectives, eventually improving both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's equipped to break down complex inquiries and reason through them in a detailed way. This guided reasoning process enables the model to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be integrated into numerous workflows such as agents, rational reasoning and information interpretation jobs.
+
DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, enabling efficient inference by routing inquiries to the most appropriate professional "clusters." This technique allows the design to concentrate on different issue domains while maintaining general efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
+
DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more effective 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, more effective designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as an instructor model.
+
You can [release](http://luodev.cn) DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and evaluate models against key security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security [controls](https://vidhiveapp.com) across your generative [AI](https://www.garagesale.es) applications.
+
Prerequisites
+
To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify 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 deploying. To ask for a limit increase, create a limit boost demand and connect to your account team.
+
Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Set up authorizations to use guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails enables you to present safeguards, prevent damaging content, and examine models against key safety criteria. You can execute security procedures for [pipewiki.org](https://pipewiki.org/wiki/index.php/User:NiamhStainforth) the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock [console](https://walnutstaffing.com) or the API. For the example code to create the guardrail, see the GitHub repo.
+
The basic circulation includes 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 to the model for reasoning. After getting the model's output, another guardrail check is applied. 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 suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate inference using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
+
1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the [navigation](https://gitlab.donnees.incubateur.anct.gouv.fr) pane. +At the time of composing this post, you can use 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 supplier and pick the DeepSeek-R1 design.
+
The model detail page supplies vital details about the design's abilities, rates structure, and application guidelines. You can find detailed use guidelines, including sample API calls and code snippets for combination. The model supports different text generation tasks, including material production, code generation, and question answering, utilizing its support finding out optimization and CoT reasoning capabilities. +The page likewise includes release options and licensing [details](https://feniciaett.com) to help you get started with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
+
You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, enter a variety of instances (between 1-100). +6. For example type, select your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function authorizations, and [encryption](http://106.14.174.2413000) settings. For a lot of utilize cases, the default settings will work well. However, for production releases, you may wish to examine these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start using the design.
+
When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive user interface where you can try out different prompts and change model criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For example, material for reasoning.
+
This is an outstanding way to check out the model's reasoning and text generation capabilities before integrating it into your applications. The playground offers instant feedback, helping you understand how the design reacts to various inputs and letting you tweak your prompts for optimum results.
+
You can quickly evaluate the design in the [play ground](https://ifairy.world) through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
+
The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the [GitHub repo](http://www.my.vw.ru). After you have actually developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, and sends out a request to produce text based upon a user timely.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production using either the UI or SDK.
+
Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical methods: utilizing the [user-friendly SageMaker](http://104.248.138.208) [JumpStart UI](https://git.hmmr.ru) or [surgiteams.com](https://surgiteams.com/index.php/User:Mirta17E66502287) implementing programmatically through the [SageMaker Python](http://1.94.30.13000) SDK. Let's explore both [techniques](https://git.coalitionofinvisiblecolleges.org) to help you pick the method that best fits your requirements.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:
+
1. On the [SageMaker](https://lius.familyds.org3000) console, select Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
+
The model browser displays available models, with details like the provider name and model abilities.
+
4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card reveals essential details, including:
+
- Model name +- Provider name +- Task [category](https://repos.ubtob.net) (for instance, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the model
+
5. Choose the model card to view the design details page.
+
The design details page consists of the following details:
+
- The model name and company details. +Deploy button to [release](http://47.108.182.667777) the design. +About and Notebooks tabs with [detailed](http://www.tomtomtextiles.com) details
+
The About tab includes [essential](https://work.melcogames.com) details, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:KindraMullings8) such as:
+
- Model description. +- License details. +- Technical specs. +- Usage guidelines
+
Before you deploy the design, it's recommended to evaluate the model details and license terms to validate compatibility with your use case.
+
6. Choose Deploy to proceed with deployment.
+
7. For Endpoint name, utilize the automatically generated name or create a [customized](https://www.klartraum-wiki.de) one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of instances (default: 1). +Selecting suitable [instance](https://223.130.175.1476501) types and counts is important for cost and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups for [precision](http://git.aimslab.cn3000). For this design, we highly suggest [adhering](https://jobskhata.com) to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
+
The implementation process can take a number of minutes to complete.
+
When deployment is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept reasoning demands through the . You can keep track of the release progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the [implementation](https://www.meetgr.com) is complete, you can invoke the design using a SageMaker runtime customer and incorporate it with your applications.
+
Deploy DeepSeek-R1 using the SageMaker Python SDK
+
To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the [required AWS](https://blazblue.wiki) consents and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
+
You can run extra requests against the predictor:
+
Implement guardrails and run inference with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a [guardrail utilizing](http://git.chilidoginteractive.com3000) the Amazon Bedrock console or [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:DianeFpt33935) the API, and execute it as displayed in the following code:
+
Clean up
+
To prevent undesirable charges, complete the steps in this area to tidy up your resources.
+
Delete the Amazon Bedrock Marketplace implementation
+
If you released the model using Amazon Bedrock Marketplace, total the following actions:
+
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace deployments. +2. In the Managed implementations section, locate the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the proper deployment: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart design you released will sustain expenses if you leave it [running](https://easy-career.com). Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
+
Conclusion
+
In this post, we checked out how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker [JumpStart](https://likemochi.com). Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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.
+
About the Authors
+
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://rabota-57.ru) companies construct innovative solutions using AWS services and sped up [compute](http://logzhan.ticp.io30000). Currently, he is focused on developing methods for fine-tuning and optimizing the reasoning performance of large language designs. In his complimentary time, Vivek takes pleasure in treking, seeing movies, and trying various foods.
+
Niithiyn Vijeaswaran is a Generative [AI](http://47.92.109.230:8080) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://ashawo.club) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://home.42-e.com:3000) with the Third-Party Model Science group at AWS.
+
Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://juryi.sn) center. She is passionate about constructing services that assist customers accelerate their [AI](https://git.kundeng.us) journey and unlock business value.
\ No newline at end of file