[SageMaker] Add warning about SageMaker SDK v3 breaking changes

docs/sagemaker/notebooks/sagemaker-sdk/deploy-embedding-models/sagemaker-notebook.ipynb

@@ -53,7 +53,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"sagemaker>=2.221.1\" --upgrade --quiet\n"
+    "!pip install \"sagemaker<3.0.0\" --upgrade --quiet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> [!WARNING]\n",
+    "> [SageMaker Python SDK v3 has been recently released](https://github.com/aws/sagemaker-python-sdk), so unless specified otherwise, all the documentation and tutorials are still using the [SageMaker Python SDK v2](https://github.com/aws/sagemaker-python-sdk/tree/master-v2). We are actively working on updating all the tutorials and examples, but in the meantime make sure to install the SageMaker SDK as `pip install \"sagemaker<3.0.0\"`."
    ]
   },
   {

docs/sagemaker/notebooks/sagemaker-sdk/deploy-llama-3-3-70b-inferentia2/sagemaker-notebook.ipynb

@@ -46,7 +46,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install sagemaker --upgrade --quiet"
+    "!pip install 'sagemaker<3.0.0' --upgrade --quiet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> [!WARNING]\n",
+    "> [SageMaker Python SDK v3 has been recently released](https://github.com/aws/sagemaker-python-sdk), so unless specified otherwise, all the documentation and tutorials are still using the [SageMaker Python SDK v2](https://github.com/aws/sagemaker-python-sdk/tree/master-v2). We are actively working on updating all the tutorials and examples, but in the meantime make sure to install the SageMaker SDK as `pip install \"sagemaker<3.0.0\"`."
    ]
   },
   {

docs/sagemaker/notebooks/sagemaker-sdk/evaluate-llm-lighteval/sagemaker-notebook.ipynb

@@ -25,7 +25,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install sagemaker --upgrade --quiet"
+    "!pip install 'sagemaker<3.0.0' --upgrade --quiet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> [!WARNING]\n",
+    "> [SageMaker Python SDK v3 has been recently released](https://github.com/aws/sagemaker-python-sdk), so unless specified otherwise, all the documentation and tutorials are still using the [SageMaker Python SDK v2](https://github.com/aws/sagemaker-python-sdk/tree/master-v2). We are actively working on updating all the tutorials and examples, but in the meantime make sure to install the SageMaker SDK as `pip install \"sagemaker<3.0.0\"`."
    ]
   },
   {

docs/sagemaker/notebooks/sagemaker-sdk/fine-tune-embedding-models/sagemaker-notebook.ipynb

@@ -39,7 +39,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install transformers \"datasets[s3]==2.18.0\" \"sagemaker>=2.190.0\" \"huggingface_hub[cli]\" --upgrade --quiet"
+    "!pip install transformers \"datasets[s3]==2.18.0\" \"sagemaker<3.0.0\" \"huggingface_hub[cli]\" --upgrade --quiet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> [!WARNING]\n",
+    "> [SageMaker Python SDK v3 has been recently released](https://github.com/aws/sagemaker-python-sdk), so unless specified otherwise, all the documentation and tutorials are still using the [SageMaker Python SDK v2](https://github.com/aws/sagemaker-python-sdk/tree/master-v2). We are actively working on updating all the tutorials and examples, but in the meantime make sure to install the SageMaker SDK as `pip install \"sagemaker<3.0.0\"`."
    ]
   },
   {

docs/sagemaker/source/dlcs/available.md

@@ -72,6 +72,9 @@ Let's say you want to use the training DLC for GPUs in
 
 The Python SagemMaker SDK util functions are not always up to date but it is much simpler than reconstructing the image URI yourself. 
 
+> [!WARNING]
+> [SageMaker Python SDK v3 has been recently released](https://github.com/aws/sagemaker-python-sdk), so unless specified otherwise, all the documentation and tutorials are still using the [SageMaker Python SDK v2](https://github.com/aws/sagemaker-python-sdk/tree/master-v2). We are actively working on updating all the tutorials and examples, but in the meantime make sure to install the SageMaker SDK as `pip install "sagemaker<3.0.0"`.
+
 ```python
 from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
 

docs/sagemaker/source/index.md

@@ -12,6 +12,9 @@ We develop new tools to simplify the adoption of custom AI accelerators like AWS
 
 By combining Hugging Face's open-source models and libraries with AWS's scalable and secure cloud services, developers can more easily and affordably incorporate advanced AI capabilities into their applications.
 
+> [!WARNING]
+> [SageMaker Python SDK v3 has been recently released](https://github.com/aws/sagemaker-python-sdk), so unless specified otherwise, all the documentation and tutorials are still using the [SageMaker Python SDK v2](https://github.com/aws/sagemaker-python-sdk/tree/master-v2). We are actively working on updating all the tutorials and examples, but in the meantime make sure to install the SageMaker SDK as `pip install "sagemaker<3.0.0"`.
+
 ## Deploy models on AWS
 
 Deploying Hugging Face models on AWS is streamlined through various services, each suited for different deployment scenarios. Here's how you can deploy your models using AWS and Hugging Face offerings.

docs/sagemaker/source/tutorials/jumpstart/jumpstart-quickstart.md

@@ -15,6 +15,9 @@ In this quickstart guide, we will deploy [Qwen/Qwen2.5-14B-Instruct](https://hug
 | SageMaker Studio domain and user profile | We recommend using SageMaker Studio for straightforward deployment and inference. Follow this [guide](https://docs.aws.amazon.com/sagemaker/latest/dg/onboard-quick-start.html). |
 | Service quotas | Most LLMs need GPU instances (e.g. ml.g5). Verify you have quota for `ml.g5.24xlarge` or [request it](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-requesting-quota-increases.html). | 
 
+> [!WARNING]
+> [SageMaker Python SDK v3 has been recently released](https://github.com/aws/sagemaker-python-sdk), so unless specified otherwise, all the documentation and tutorials are still using the [SageMaker Python SDK v2](https://github.com/aws/sagemaker-python-sdk/tree/master-v2). We are actively working on updating all the tutorials and examples, but in the meantime make sure to install the SageMaker SDK as `pip install "sagemaker<3.0.0"`.
+
 ## 2· Endpoint deployment
 
 Let's explain how you would deploy a Hugging Face model to SageMaker browsing through the Jumpstart catalog:

docs/sagemaker/source/tutorials/sagemaker-sdk/deploy-sagemaker-sdk.md

@@ -32,9 +32,12 @@ To start training locally, you need to setup an appropriate [IAM role](https://d
 Upgrade to the latest `sagemaker` version.
 
 ```bash
-pip install sagemaker --upgrade
+pip install 'sagemaker<3.0.0'
 ```
 
+> [!WARNING]
+> [SageMaker Python SDK v3 has been recently released](https://github.com/aws/sagemaker-python-sdk), so unless specified otherwise, all the documentation and tutorials are still using the [SageMaker Python SDK v2](https://github.com/aws/sagemaker-python-sdk/tree/master-v2). We are actively working on updating all the tutorials and examples, but in the meantime make sure to install the SageMaker SDK as `pip install "sagemaker<3.0.0"`.
+
 **SageMaker environment**
 
 Setup your SageMaker environment as shown below:

docs/sagemaker/source/tutorials/sagemaker-sdk/sagemaker-sdk-quickstart.md

@@ -11,9 +11,12 @@ The get started guide will show you how to quickly use Hugging Face on Amazon Sa
 Get started by installing the necessary Hugging Face libraries and SageMaker. You will also need to install [PyTorch](https://pytorch.org/get-started/locally/) if you don't already have it installed. If you run this example in SageMaker Studio, it is already installed in the notebook kernel!
 
 ```python
-pip install "sagemaker>=2.140.0" "transformers==4.26.1" "datasets[s3]==2.10.1" --upgrade
+pip install "sagemaker<3.0.0" "transformers==4.26.1" "datasets[s3]==2.10.1" --upgrade
 ```
 
+> [!WARNING]
+> [SageMaker Python SDK v3 has been recently released](https://github.com/aws/sagemaker-python-sdk), so unless specified otherwise, all the documentation and tutorials are still using the [SageMaker Python SDK v2](https://github.com/aws/sagemaker-python-sdk/tree/master-v2). We are actively working on updating all the tutorials and examples, but in the meantime make sure to install the SageMaker SDK as `pip install "sagemaker<3.0.0"`.
+
 If you want to run this example in [SageMaker Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/studio.html), upgrade [ipywidgets](https://ipywidgets.readthedocs.io/en/latest/) for the 🤗 Datasets library and restart the kernel:
 
 ```python

docs/sagemaker/source/tutorials/sagemaker-sdk/training-sagemaker-sdk.md

@@ -30,9 +30,12 @@ To start training locally, you need to setup an appropriate [IAM role](https://d
 Upgrade to the latest `sagemaker` version:
 
 ```bash
-pip install sagemaker --upgrade
+pip install 'sagemaker<3.0.0'
 ```
 
+> [!WARNING]
+> [SageMaker Python SDK v3 has been recently released](https://github.com/aws/sagemaker-python-sdk), so unless specified otherwise, all the documentation and tutorials are still using the [SageMaker Python SDK v2](https://github.com/aws/sagemaker-python-sdk/tree/master-v2). We are actively working on updating all the tutorials and examples, but in the meantime make sure to install the SageMaker SDK as `pip install "sagemaker<3.0.0"`.
+
 **SageMaker environment**
 
 Setup your SageMaker environment as shown below: