commit 39e6a6c8a026171b701ceda72b251ca4a149c34c Author: estebanservice Date: Tue Apr 8 16:14:33 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..3542cf7 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are delighted 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://code.dsconce.space)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative [AI](https://35.237.164.2) ideas on AWS.
+
In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the models too.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](http://gsend.kr) that utilizes support learning to [enhance thinking](https://lab.chocomart.kz) capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying function is its support learning (RL) action, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:GiaStapylton) which was used to refine the design's responses beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, suggesting it's geared up to break down intricate queries and factor through them in a detailed manner. This guided reasoning process enables the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:SusanneDodson) user interaction. With its comprehensive abilities DeepSeek-R1 has captured the market's attention as a [versatile text-generation](https://baescout.com) design that can be integrated into different workflows such as agents, [logical reasoning](https://prsrecruit.com) and information interpretation tasks.
+
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows [activation](https://subamtv.com) of 37 billion parameters, enabling efficient reasoning by routing queries to the most [relevant specialist](http://drive.ru-drive.com) "clusters." This approach enables the design to focus on different problem domains while maintaining overall effectiveness. DeepSeek-R1 requires 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](http://bedfordfalls.live) the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
+
DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective designs to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor model.
+
You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](https://avicii.blog) model, we suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and assess models against crucial security requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](https://jamesrodriguezclub.com) only the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](https://cloudsound.ideiasinternet.com) applications.
+
Prerequisites
+
To release 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, select Amazon SageMaker, and confirm 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 releasing. To request a limit boost, develop a limitation boost demand and reach out to your account group.
+
Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the [correct](https://admithel.com) AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Establish authorizations to utilize guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails allows you to introduce safeguards, prevent harmful content, and examine models against crucial security requirements. You can implement safety steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to [examine](http://vts-maritime.com) user inputs and model actions deployed 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 produce the guardrail, see the GitHub repo.
+
The basic flow includes the following actions: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for inference. After receiving the model'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 intervened 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 sections demonstrate reasoning using this API.
+
Deploy DeepSeek-R1 in [Amazon Bedrock](https://wisewayrecruitment.com) Marketplace
+
Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
+
1. On the Amazon Bedrock console, select Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.
+
The model detail page offers vital details about the model's capabilities, pricing structure, and application standards. You can discover detailed usage directions, [yewiki.org](https://www.yewiki.org/User:HamishKidman) including sample API calls and code snippets for combination. The model supports different text generation jobs, consisting of material creation, code generation, and question answering, using its support finding out optimization and CoT thinking [abilities](http://www.thynkjobs.com). +The page also consists of implementation options 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 configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For [Variety](https://japapmessenger.com) of circumstances, get in a number of [circumstances](http://test.wefanbot.com3000) (between 1-100). +6. For example type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role authorizations, and encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you may want to examine these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
+
When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +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, utilize DeepSeek's chat design template for optimal outcomes. For instance, content for inference.
+
This is an excellent way to check out the [model's reasoning](https://edge1.co.kr) and text generation abilities before incorporating it into your applications. The play area offers instant feedback, helping you understand how the design responds to various inputs and letting you tweak your triggers for optimal outcomes.
+
You can rapidly check the model in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
+
Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
+
The following code example demonstrates how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to implement guardrails. The script [initializes](https://gitlab.digineers.nl) the bedrock_runtime client, configures reasoning specifications, [surgiteams.com](https://surgiteams.com/index.php/User:DarrellBaldwinso) and sends a demand to produce text based upon a user prompt.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into [production](http://123.60.97.16132768) using either the UI or SDK.
+
Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you pick the technique that finest suits your requirements.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following [actions](https://www.homebasework.net) to release DeepSeek-R1 [utilizing SageMaker](https://mediawiki1263.00web.net) JumpStart:
+
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](http://42.192.14.1353000) in the navigation pane.
+
The model internet browser displays available designs, with details like the [provider](https://www.suyun.store) name and model abilities.
+
4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card [reveals crucial](http://49.50.103.174) details, including:
+
- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon [Bedrock APIs](https://han2.kr) to invoke the model
+
5. Choose the model card to see the design details page.
+
The design details page includes the following details:
+
- The design name and supplier details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
+
The About tab consists of important details, such as:
+
- Model description. +- License details. +- Technical requirements. +- Usage standards
+
Before you deploy the design, it's suggested to evaluate the model details and license terms to validate compatibility with your use case.
+
6. Choose Deploy to continue with release.
+
7. For Endpoint name, utilize the instantly generated name or create a custom-made one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of circumstances (default: 1). +Selecting proper [instance types](http://101.132.182.1013000) and counts is essential for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we highly suggest adhering to [SageMaker JumpStart](http://git.jishutao.com) default settings and making certain that network isolation remains in place. +11. [Choose Deploy](https://git.intelgice.com) to deploy the model.
+
The implementation process can take numerous minutes to complete.
+
When release is complete, your endpoint status will change to InService. At this moment, the model is ready to accept inference requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can conjure up the model using a SageMaker runtime client and [wiki.myamens.com](http://wiki.myamens.com/index.php/User:ConcepcionMoriar) integrate it with your applications.
+
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
+
To get begun with DeepSeek-R1 [utilizing](https://www.uaehire.com) the SageMaker Python SDK, you will need 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 reasoning programmatically. The code for releasing the model is in the Github here. You can clone the note pad 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 utilize the ApplyGuardrail API with your [SageMaker JumpStart](http://www.topverse.world3000) predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
+
Clean up
+
To prevent undesirable charges, finish the actions in this section to tidy up your resources.
+
Delete the Amazon Bedrock Marketplace release
+
If you released the model using Amazon Bedrock Marketplace, complete the following steps:
+
1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace implementations. +2. In the Managed releases area, locate the endpoint you wish 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 right deployment: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart design you deployed will sustain costs if you leave it running. Use the following code to erase the endpoint if you wish 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. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to 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.
+
About the Authors
+
Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://gitlab.code-nav.cn) business construct innovative options utilizing AWS services and accelerated calculate. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of large language designs. In his spare time, Vivek delights in hiking, watching movies, and attempting different foods.
+
Niithiyn Vijeaswaran is a Generative [AI](http://www.homeserver.org.cn:3000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://jobedges.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://swwwwiki.coresv.net) with the Third-Party Model Science group at AWS.
+
Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://cloudsound.ideiasinternet.com) center. She is enthusiastic about constructing solutions that assist consumers accelerate their [AI](https://git-dev.xyue.zip:8443) journey and unlock service worth.
\ No newline at end of file