From ec1a49d1c04115530dd40034d43229f407a790dc Mon Sep 17 00:00:00 2001 From: katehytten3273 Date: Sat, 12 Apr 2025 00:26:35 +0800 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..607e5b9 --- /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 reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock [Marketplace](https://telecomgurus.in) and Amazon SageMaker [JumpStart](https://skillsvault.co.za). With this launch, you can now deploy DeepSeek [AI](http://test.9e-chain.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](https://powerstack.co.in) concepts on AWS.
+
In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the [distilled versions](https://www.vfrnds.com) of the designs also.
+
Overview of DeepSeek-R1
+
DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://www.trappmasters.com) that utilizes reinforcement [finding](http://advance5.com.my) out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying feature is its support learning (RL) step, which was [utilized](https://hgarcia.es) to improve the design's reactions beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt better to user [feedback](http://stockzero.net) and objectives, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 [utilizes](http://private.flyautomation.net82) a chain-of-thought (CoT) technique, implying it's [equipped](http://files.mfactory.org) to break down complex queries and reason through them in a detailed way. This guided reasoning [procedure enables](http://39.106.223.11) the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be [integrated](http://47.95.167.2493000) into various workflows such as agents, rational thinking and data interpretation jobs.
+
DeepSeek-R1 [utilizes](http://34.236.28.152) a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, making it possible for effective inference by routing questions to the most relevant expert "clusters." This approach permits the model to concentrate on different issue domains while maintaining total effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 [GPUs offering](http://47.107.92.41234) 1128 GB of GPU memory.
+
DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more [efficient architectures](https://livy.biz) based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of [training](https://161.97.85.50) smaller, more effective models to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as a [teacher model](http://118.195.204.2528080).
+
You can deploy DeepSeek-R1 model 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 site, we will use Amazon Bedrock Guardrails to [introduce](https://wik.co.kr) safeguards, avoid harmful content, and evaluate models against key security requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://alllifesciences.com) applications.
+
Prerequisites
+
To deploy the DeepSeek-R1 model, 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, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) pick 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 deploying. To request a limit increase, create a limit increase demand and connect to your account group.
+
Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish consents to use guardrails for material filtering.
+
Implementing guardrails with the ApplyGuardrail API
+
Amazon Bedrock Guardrails [permits](https://members.mcafeeinstitute.com) you to introduce safeguards, avoid damaging content, and assess designs against crucial safety criteria. You can execute precaution for the DeepSeek-R1 model utilizing the Amazon [Bedrock ApplyGuardrail](https://git.obo.cash) API. This permits you to use guardrails to assess user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon [Bedrock console](https://social.stssconstruction.com) or the API. For the example code to develop 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. If the input passes the [guardrail](https://radicaltarot.com) 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 final check, it's [returned](http://szyg.work3000) as the result. 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 took place at the input or output stage. The [examples showcased](http://www.gz-jj.com) in the following areas demonstrate reasoning using this API.
+
Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
+
Amazon Bedrock Marketplace provides 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](https://www.emploitelesurveillance.fr) brochure under Foundation models in the navigation pane. +At the time of composing 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 [company](https://surreycreepcatchers.ca) and pick the DeepSeek-R1 model.
+
The model detail page supplies vital details about the design's capabilities, pricing structure, and execution guidelines. You can discover detailed use guidelines, consisting of sample API calls and code bits for combination. The design supports numerous text generation jobs, consisting of content production, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking abilities. +The page also includes implementation alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
+
You will be triggered to set up the release 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 of instances, go into a number of circumstances (in between 1-100). +6. For example type, choose your circumstances type. For optimal efficiency with DeepSeek-R1, a [GPU-based instance](http://121.5.25.2463000) type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For most use cases, the default settings will work well. However, for production releases, you may want to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
+
When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive user interface where you can explore various triggers and adjust design criteria like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal outcomes. For example, content for reasoning.
+
This is an exceptional method to check out the design's thinking and text generation capabilities before incorporating it into your applications. The play area provides instant feedback, assisting you comprehend how the design reacts to various inputs and letting you fine-tune your triggers for ideal outcomes.
+
You can quickly evaluate the model in the playground through the UI. However, to conjure up the [deployed design](https://www.menacopt.com) programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
+
Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
+
The following code example shows how to carry out inference utilizing a [released](https://www.stormglobalanalytics.com) DeepSeek-R1 model through [Amazon Bedrock](http://47.56.181.303000) utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference specifications, and sends out a demand to create 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](https://ugit.app) ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into [production](http://94.191.73.383000) using either the UI or SDK.
+
Deploying DeepSeek-R1 design through SageMaker JumpStart provides two hassle-free methods: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the approach that best suits your needs.
+
Deploy DeepSeek-R1 through SageMaker JumpStart UI
+
Complete the following steps to release DeepSeek-R1 using [SageMaker](http://vimalakirti.com) JumpStart:
+
1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:GenieFaulding06) select JumpStart in the navigation pane.
+
The model internet browser shows available models, with details like the company name and design abilities.
+
4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card reveals key details, consisting of:
+
- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if relevant), indicating 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](https://www.kukustream.com) to view the design details page.
+
The model details page consists of the following details:
+
- The model name and provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
+
The About tab includes important details, such as:
+
- Model description. +- License details. +- Technical specs. +- Usage guidelines
+
Before you release the model, it's recommended to review the design details and license terms to verify compatibility with your use case.
+
6. Choose Deploy to continue with implementation.
+
7. For Endpoint name, utilize the instantly produced name or create a custom one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting proper circumstances types and counts is important for expense and efficiency optimization. Monitor your release 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 [precision](https://xremit.lol). For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network [seclusion](http://201.17.3.963000) remains in place. +11. Choose Deploy to deploy the design.
+
The deployment process can take several minutes to finish.
+
When implementation is complete, your endpoint status will alter to InService. At this moment, the design is all set to accept reasoning requests through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is complete, you can invoke the design using a SageMaker runtime client and incorporate 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 require to set up the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
+
You can run extra demands against the predictor:
+
Implement guardrails and run reasoning 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 utilizing the Amazon [Bedrock console](https://tjoobloom.com) or the API, and implement it as displayed in the following code:
+
Clean up
+
To avoid [unwanted](https://www.punajuaj.com) charges, complete the steps in this area to clean up your resources.
+
Delete the Amazon Bedrock Marketplace release
+
If you released the design utilizing Amazon Bedrock Marketplace, complete the following steps:
+
1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments. +2. In the Managed implementations section, find the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the correct release: 1. Endpoint name. +2. Model name. +3. Endpoint status
+
Delete the SageMaker JumpStart predictor
+
The SageMaker JumpStart model you released 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 explored 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 begin. 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 is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://quickdatescript.com) companies develop innovative services using AWS services and [accelerated](http://82.157.77.1203000) calculate. Currently, he is concentrated on developing techniques for fine-tuning and [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:AlejandraFison2) enhancing the inference efficiency of large language designs. In his totally free time, Vivek enjoys hiking, seeing motion pictures, and attempting different foods.
+
Niithiyn Vijeaswaran is a Generative [AI](https://safeway.com.bd) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://charin-issuedb.elaad.io) 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](http://energonspeeches.com) with the Third-Party Model Science group at AWS.
+
Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://zeus.thrace-lan.info:3000) hub. She is enthusiastic about developing options that help customers accelerate their [AI](https://git.rell.ru) [journey](https://git.tesinteractive.com) and unlock company value.
\ No newline at end of file