commit 4aebf1e389c5867aeabdb8ee32f0e80a9011dcea Author: alinacruse3226 Date: Tue Feb 18 05:46:16 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..331312e --- /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 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://namoshkar.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](http://ieye.xyz:5080) ideas 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 deploy the distilled variations of the designs also.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://15.164.25.185) that utilizes support discovering to [enhance thinking](https://maram.marketing) abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key identifying feature is its support knowing (RL) step, which was utilized to fine-tune the design's actions beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's geared up to break down complicated questions and reason through them in a detailed manner. This directed reasoning [process](http://115.29.202.2468888) [permits](http://60.205.210.36) the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, sensible reasoning and information interpretation jobs.
+
DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, making it possible for efficient reasoning by routing queries to the most appropriate expert "clusters." This technique enables the model to concentrate on various problem domains while maintaining general effectiveness. DeepSeek-R1 requires 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](https://vybz.live) to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the thinking [abilities](https://celticfansclub.com) of the main R1 design to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective designs to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 design, using it as an instructor model.
+
You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and examine models against essential safety requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://horizonsmaroc.com) applications.
+
Prerequisites
+
To release the DeepSeek-R1 model, you require 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 verify you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit increase, develop a [limit increase](https://29sixservices.in) request and reach out to your account group.
+
Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Establish authorizations to use guardrails for content filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging material, and examine models against essential security criteria. You can carry out security steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
+
The involves 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 out to the model for reasoning. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the [outcome](http://tigg.1212321.com). 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 phase. The examples showcased in the following areas demonstrate reasoning using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace gives 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 actions:
+
1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock [tooling](https://adremcareers.com). +2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 design.
+
The model detail page supplies vital [details](https://git.opskube.com) about the design's capabilities, rates structure, and implementation guidelines. You can find detailed usage directions, including sample API calls and code bits for combination. The design supports various text generation tasks, consisting of content creation, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning capabilities. +The page also includes deployment choices and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
+
You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a number of instances (between 1-100). +6. For example type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can [configure innovative](https://arlogjobs.org) security and infrastructure settings, including virtual private cloud (VPC) networking, service role approvals, and file encryption [settings](http://git.youkehulian.cn). For a lot of [utilize](https://owangee.com) cases, the default settings will work well. However, for production releases, you might want to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
+
When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive user interface where you can explore different prompts and change design parameters like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For instance, content for inference.
+
This is an excellent method to explore the model's thinking and text generation capabilities before integrating it into your applications. The play ground provides immediate feedback, helping you understand how the model reacts to numerous inputs and letting you tweak your triggers for ideal results.
+
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 reasoning using guardrails with the released DeepSeek-R1 endpoint
+
The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to [implement guardrails](http://35.207.205.183000). The script initializes the bedrock_runtime client, configures reasoning criteria, and sends out a demand to produce text based on a user prompt.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) center with FMs, [integrated](https://foris.gr) algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
+
Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free approaches: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to assist you choose the approach that [finest fits](https://www.eadvisor.it) your needs.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
+
1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
+
The model internet browser displays available models, with details like the provider name and model abilities.
+
4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows key details, including:
+
[- Model](https://andonovproltd.com) name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model
+
5. Choose the design card to see the model details page.
+
The model details page includes the following details:
+
- The design name and company details. +[Deploy button](https://krotovic.cz) to [release](http://dkjournal.co.kr) the design. +About and Notebooks tabs with detailed details
+
The About tab consists of essential details, such as:
+
- Model description. +- License details. +- Technical requirements. +- Usage standards
+
Before you deploy the model, it's suggested to examine the model details and license terms to verify compatibility with your usage case.
+
6. Choose Deploy to continue with deployment.
+
7. For Endpoint name, use the automatically produced name or produce a customized one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of circumstances (default: 1). +Selecting suitable circumstances types and counts is essential for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the model.
+
The release process can take a number of minutes to complete.
+
When release is total, your endpoint status will change to [InService](https://diskret-mote-nodeland.jimmyb.nl). At this moment, the design is all set to accept inference [requests](http://admin.youngsang-tech.com) through the endpoint. You can monitor the [implementation development](https://energypowerworld.co.uk) on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the [deployment](https://e-sungwoo.co.kr) is total, you can invoke the design utilizing a SageMaker runtime customer and integrate it with your [applications](http://dev.nextreal.cn).
+
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
+
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 and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is provided in the Github here. You can clone the notebook and [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/bagjanine969) run from SageMaker Studio.
+
You can run additional demands against the predictor:
+
Implement guardrails and run inference with your SageMaker JumpStart predictor
+
Similar to Amazon Bedrock, you can likewise 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 avoid undesirable charges, finish the steps in this area to clean up your resources.
+
Delete the Amazon Bedrock Marketplace release
+
If you released the design using Amazon Bedrock Marketplace, complete the following steps:
+
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. +2. In the Managed releases section, 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 deleting the proper release: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://techport.io) status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart design you released will sustain costs if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
+
Conclusion
+
In this post, we explored how you can access and deploy the DeepSeek-R1 model using [Bedrock Marketplace](https://ifin.gov.so) and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or [Amazon Bedrock](https://chancefinders.com) Marketplace now to get begun. For more details, describe Use Amazon Bedrock [tooling](https://asicwiki.org) with Amazon SageMaker JumpStart models, SageMaker [JumpStart pretrained](https://juryi.sn) designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.
+
About the Authors
+
Vivek Gangasani is a Lead Specialist Solutions Architect for [Inference](https://bdstarter.com) at AWS. He assists emerging generative [AI](http://223.68.171.150:8004) [companies construct](https://foxchats.com) ingenious solutions utilizing AWS services and sped up calculate. Currently, he is focused on developing methods for fine-tuning and optimizing the inference efficiency of large language models. In his leisure time, Vivek enjoys treking, watching movies, and trying different foods.
+
Niithiyn Vijeaswaran is a Generative [AI](https://www.muslimtube.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://jimsusefultools.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
+
Jonathan Evans is a Professional Solutions Architect working on generative [AI](http://111.160.87.82:8004) with the Third-Party Model Science team at AWS.
+
Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://justhired.co.in) center. She is passionate about building options that help consumers accelerate their [AI](https://teachersconsultancy.com) journey and unlock business worth.
\ No newline at end of file