From 3516125843b2ce42b63b6fee762ddb9d8fc48fc7 Mon Sep 17 00:00:00 2001 From: carenprater27 Date: Sun, 9 Feb 2025 20:52:13 +0800 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-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..260b48d --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are thrilled 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://gogs.jublot.com)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://gitoa.ru) 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 deploy the [distilled versions](http://kousokuwiki.org) of the models as well.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://demo.pixelphotoscript.com) that utilizes reinforcement learning to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating function is its support knowing (RL) step, which was used to fine-tune the model's reactions beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex questions and reason through them in a detailed way. This directed reasoning process permits the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, rational thinking and data analysis jobs.
+
DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, making it possible for effective reasoning by [routing questions](http://106.15.120.1273000) to the most relevant specialist "clusters." This [method permits](https://gitea.scalz.cloud) the design to focus on different issue domains while maintaining total [performance](http://63.32.145.226). DeepSeek-R1 needs a minimum of 800 GB of [HBM memory](https://git.epochteca.com) in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of [GPU memory](https://parejas.teyolia.mx).
+
DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more efficient architectures based upon popular open [designs](https://munidigital.iie.cl) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient designs to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as an [instructor design](http://47.101.187.298081).
+
You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and [examine models](https://clubamericafansclub.com) against essential security criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can [develop](https://www.nairaland.com) numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative [AI](http://47.107.29.61:3000) applications.
+
Prerequisites
+
To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon 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 circumstances in the AWS Region you are releasing. To request a limit boost, develop a limit increase request and reach out to your account group.
+
Because you will be releasing 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 Establish [approvals](https://tmsafri.com) to utilize guardrails for material filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails permits you to introduce safeguards, prevent damaging content, and assess designs against essential security requirements. You can carry out [safety steps](http://youtubeer.ru) for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to [apply guardrails](http://duberfly.com) to assess user inputs and design responses released on Amazon Bedrock [Marketplace](http://git.sdkj001.cn) and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
+
The general 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 inference. 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 intervened by the guardrail, a message is returned indicating the nature of the [intervention](https://www.flughafen-jobs.com) and whether it took place at the input or [output phase](https://sneakerxp.com). The examples showcased in the following sections 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 models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
+
1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane. +At the time of composing 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 supplier and select the DeepSeek-R1 design.
+
The design detail page offers essential details about the design's abilities, pricing structure, and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:GemmaJenson1) application guidelines. You can find detailed usage instructions, including sample API calls and code snippets for combination. The design supports different text generation jobs, consisting of material creation, code generation, and question answering, utilizing its [support finding](https://gitea.scalz.cloud) out optimization and CoT reasoning capabilities. +The page also consists of implementation options and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, select Deploy.
+
You will be prompted to set up 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 of instances, get in a variety of instances (in between 1-100). +6. For example type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is [suggested](https://es-africa.com). +Optionally, you can set up innovative security and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to align with your organization's security and compliance [requirements](http://24.233.1.3110880). +7. Choose Deploy to begin using the design.
+
When the implementation is total, you can check 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 various prompts and adjust model specifications like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For example, material for inference.
+
This is an excellent way to explore the design's reasoning and text generation capabilities before incorporating it into your applications. The play area offers instant feedback, helping you comprehend how the design reacts to different inputs and letting you fine-tune your triggers for optimum results.
+
You can quickly test the design in the [playground](https://humped.life) through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
+
Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
+
The following code example demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, [utilize](https://www.valeriarp.com.tr) the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:FlorenceGuillen) sends out a request to [produce text](http://kandan.net) based upon a user timely.
+
Deploy DeepSeek-R1 with SageMaker JumpStart
+
SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production using either the UI or SDK.
+
Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free techniques: using the intuitive SageMaker JumpStart UI or [implementing programmatically](http://121.37.208.1923000) through the SageMaker Python SDK. Let's check out both techniques to help you pick the approach that [finest suits](https://service.aicloud.fit50443) your needs.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
+
1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
+
The design internet browser displays available models, with details like the supplier name and model capabilities.
+
4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals essential details, consisting of:
+
- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if applicable), [suggesting](http://cjma.kr) that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the model
+
5. Choose the model card to view the model details page.
+
The design details page consists of the following details:
+
- The design name and service provider [details](https://git.pleasantprogrammer.com). +Deploy button to release the design. +About and Notebooks tabs with detailed details
+
The About tab includes essential details, such as:
+
- Model description. +- License details. +- Technical specifications. +- Usage standards
+
Before you deploy the model, it's suggested to evaluate the design details and license terms to confirm compatibility with your usage case.
+
6. Choose Deploy to continue with deployment.
+
7. For Endpoint name, use the immediately created name or produce a customized one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the number of circumstances (default: 1). +Selecting appropriate circumstances types and counts is important for expense and efficiency optimization. Monitor your deployment to adjust 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 accuracy. For this design, we strongly advise sticking to SageMaker JumpStart [default settings](https://edge1.co.kr) and making certain that network isolation remains in place. +11. Choose Deploy to deploy the model.
+
The release procedure can take a number of minutes to finish.
+
When implementation is complete, your endpoint status will alter to InService. At this point, the design is all set to accept inference requests through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is complete, you can conjure up the design using a SageMaker runtime client and integrate it with your applications.
+
Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
+
To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a [guardrail](http://106.52.126.963000) using the Amazon Bedrock console or the API, and implement it as shown in the following code:
+
Clean up
+
To prevent undesirable charges, finish the [actions](https://kollega.by) in this area to tidy up your resources.
+
Delete the Amazon Bedrock Marketplace implementation
+
If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
+
1. On the Amazon Bedrock console, under [Foundation designs](http://47.108.161.783000) in the navigation pane, select Marketplace [deployments](http://94.191.73.383000). +2. In the Managed releases section, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the correct implementation: 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. Use the following code to delete the endpoint if you wish to stop sustaining charges. For more details, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:Bettina5096) see Delete Endpoints and Resources.
+
Conclusion
+
In this post, we checked out how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. [Visit SageMaker](https://1samdigitalvision.com) JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
+
About the Authors
+
[Vivek Gangasani](https://www.rhcapital.cl) is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://code.webpro.ltd) [business build](https://szmfettq2idi.com) ingenious services utilizing AWS services and sped up calculate. Currently, he is focused on establishing methods for fine-tuning and enhancing the inference efficiency of big language models. In his downtime, Vivek delights in hiking, seeing movies, and trying various foods.
+
Niithiyn Vijeaswaran is a [Generative](http://dibodating.com) [AI](https://hotjobsng.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://47.101.187.29:8081) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
+
Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://mixup.wiki) with the Third-Party Model Science group at AWS.
+
Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gitea.thuispc.dynu.net) center. She is enthusiastic about constructing services that assist customers accelerate their [AI](https://git2.ujin.tech) journey and unlock service value.
\ No newline at end of file