commit 98ef89ee2ae79ab6864f4b01c12e1529fbe2418d Author: claudette9162 Date: Fri Apr 4 13:29:17 2025 +0800 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart 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..0a80e46 --- /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 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 release DeepSeek [AI](https://mssc.ltd)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative [AI](https://jobsite.hu) concepts on AWS.
+
In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the designs as well.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://git.jzmoon.com) that utilizes support finding out to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating feature is its support knowing (RL) step, which was used to fine-tune the design's responses beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, eventually improving both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's geared up to break down complex inquiries and factor through them in a detailed way. This guided reasoning procedure permits the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has recorded the [market's attention](https://phoebe.roshka.com) as a [flexible text-generation](http://sdongha.com) model that can be incorporated into numerous workflows such as agents, sensible thinking and information analysis tasks.
+
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion specifications, making it possible for effective reasoning by routing questions to the most pertinent expert "clusters." This technique allows the design to concentrate on different problem domains while maintaining total performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 [xlarge circumstances](http://povoq.moe1145) to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient models to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.
+
You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and evaluate designs against key safety criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](https://vcanhire.com) [applications](https://letsstartjob.com).
+
Prerequisites
+
To deploy the DeepSeek-R1 design, you need 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 verify you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 [xlarge instance](http://www.xn--v42bq2sqta01ewty.com) in the AWS Region you are deploying. To request a limitation increase, produce a limit boost request and connect to your account group.
+
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Establish permissions to utilize guardrails for material filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging material, and assess designs against crucial security requirements. You can implement security measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a [guardrail](https://securityjobs.africa) utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
+
The general circulation involves the following steps: First, the system receives an input for the design. This input is then processed through the [ApplyGuardrail API](http://git.e365-cloud.com). If the input passes the guardrail check, it's sent out to the model for inference. After receiving the model's output, another [guardrail check](https://livy.biz) is used. If the output passes this last check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas demonstrate reasoning using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
+
1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.
+
The model detail page provides necessary details about the design's capabilities, prices structure, and application standards. You can discover detailed usage instructions, including sample API calls and code bits for combination. The design supports different text generation tasks, including material development, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning abilities. +The page also includes release choices and licensing details to help you get started with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, select Deploy.
+
You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, get in a number of instances (between 1-100). +6. For example type, choose your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For many utilize cases, the [default settings](https://pingpe.net) will work well. However, for production deployments, you may wish to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
+
When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can explore different triggers and change model parameters 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, content for inference.
+
This is an exceptional way to [explore](https://gogs.lnart.com) the design's thinking and text generation capabilities before incorporating it into your applications. The play ground supplies instant feedback, [assisting](http://git.techwx.com) you understand [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:SIAMaryellen) how the model reacts to numerous inputs and letting you fine-tune your [prompts](https://www.frigorista.org) for [optimal outcomes](http://123.207.52.1033000).
+
You can quickly test the design in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run reasoning utilizing guardrails with the [deployed](https://git.jzmoon.com) DeepSeek-R1 endpoint
+
The following code example [demonstrates](https://almanyaisbulma.com.tr) how to carry out reasoning utilizing a deployed DeepSeek-R1 model through [Amazon Bedrock](https://teachinthailand.org) using the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock [console](https://younetwork.app) or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, [utilize](https://rootsofblackessence.com) the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, and sends out a [request](http://39.100.93.1872585) to create [text based](http://gitlab.hanhezy.com) on 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 just a few clicks. With [SageMaker](https://playvideoo.com) JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production using either the UI or SDK.
+
Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical approaches: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you select the method that best fits your needs.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following steps to release DeepSeek-R1 [utilizing SageMaker](https://innovator24.com) JumpStart:
+
1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
+
The design internet browser displays available designs, with details like the company name and model capabilities.
+
4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals essential details, including:
+
- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
+
5. Choose the model card to see the design details page.
+
The [design details](https://newsfast.online) page consists of the following details:
+
- The design name and company details. +Deploy button to release the model. +About and Notebooks tabs with detailed details
+
The About tab consists of essential details, such as:
+
- Model description. +- License details. +- Technical [specifications](http://git.indep.gob.mx). +- Usage standards
+
Before you release the design, it's recommended to review the model details and license terms to confirm compatibility with your use case.
+
6. Choose Deploy to proceed with implementation.
+
7. For Endpoint name, utilize the instantly generated name or produce a customized one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the number of circumstances (default: 1). +Selecting appropriate instance types and counts is important for cost and performance optimization. Monitor your release to change these settings as needed.Under Inference type, [Real-time reasoning](https://clearcreek.a2hosted.com) is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the design.
+
The implementation process can take numerous minutes to complete.
+
When implementation is complete, your endpoint status will alter to InService. At this point, the design is ready to accept reasoning requests through the endpoint. You can keep track of the deployment development on the SageMaker [console Endpoints](https://cannabisjobs.solutions) page, which will show appropriate metrics and status details. When the [implementation](https://rejobbing.com) is total, you can invoke the model utilizing a [SageMaker](https://elitevacancies.co.za) runtime customer and integrate it with your applications.
+
Deploy DeepSeek-R1 using the SageMaker Python SDK
+
To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the [SageMaker](http://47.119.128.713000) Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for . The code for releasing the design is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
+
You can run extra demands 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 create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
+
Clean up
+
To prevent unwanted charges, complete the actions in this area to clean up your resources.
+
Delete the Amazon Bedrock Marketplace implementation
+
If you [deployed](http://47.116.115.15610081) the model utilizing [Amazon Bedrock](https://git.andrewnw.xyz) Marketplace, total the following actions:
+
1. On the Amazon Bedrock console, under [Foundation](https://gitlab.damage.run) designs in the navigation pane, choose Marketplace implementations. +2. In the Managed deployments section, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the right deployment: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart model you released 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.
+
Conclusion
+
In this post, we checked out how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](https://holisticrecruiters.uk) or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, [Amazon SageMaker](http://116.205.229.1963000) JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.
+
About the Authors
+
[Vivek Gangasani](https://newsfast.online) is a Lead Specialist Solutions Architect for Inference at AWS. He [helps emerging](https://git.io8.dev) generative [AI](https://prantle.com) companies build ingenious solutions utilizing AWS services and accelerated calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the reasoning performance of big language designs. In his downtime, Vivek takes pleasure in treking, seeing movies, and attempting various foods.
+
Niithiyn Vijeaswaran is a Generative [AI](https://guridentwell.com) Specialist Solutions Architect with the Third-Party Model [Science team](http://220.134.104.928088) at AWS. His area of focus is AWS [AI](https://www.cbtfmytube.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://saghurojobs.com) with the Third-Party Model Science team at AWS.
+
Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.lolilove.rs) hub. She is passionate about constructing services that help clients accelerate their [AI](https://video.lamsonsaovang.com) [journey](https://investsolutions.org.uk) and unlock organization value.
\ No newline at end of file