diff --git a/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/albert-base-v2/albert-base-v2.ipynb b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/albert-base-v2/albert-base-v2.ipynb
index c9b611aa3c..3eb085c4bb 100644
--- a/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/albert-base-v2/albert-base-v2.ipynb
+++ b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/albert-base-v2/albert-base-v2.ipynb
@@ -38,11 +38,11 @@
     "\n",
     "## Introduction\n",
     "\n",
-    "This example notebook demonstrates how to compile and fine-tune a question and answering NLP task. We use Hugging Face's `transformers` and `datasets` libraries with Amazon Sagemaker Training Compiler to accelerate fine-tuning of a pre-trained transformer model on question and answering. In particular, the pre-trained model will be fine-tuned using the `SQuAD` dataset. To get started, we need to set up the environment with a few prerequisite steps to add permissions, configurations, and so on. \n",
+    "This example notebook demonstrates how to compile and fine-tune a question and answering NLP task. We use HuggingFace's transformers and datasets libraries with Amazon SageMaker Training Compiler to accelerate fine-tuning of a pre-trained transformer model on question and answering. In particular, the pre-trained model will be fine-tuned using the SQuAD dataset. To get started, we need to set up the environment with a few prerequisite steps to add permissions, configurations, and so on. \n",
     "\n",
-    "**NOTE:** You can run this demo in SageMaker Studio, SageMaker notebook instances, or your local machine with AWS CLI set up. If using SageMaker Studio or SageMaker notebook instances, make sure you choose one of the PyTorch-based kernels, `Python 3 (PyTorch x.y Python 3.x CPU Optimized)` or `conda_pytorch_p36` respectively.\n",
+    "**NOTE:** You can run this demo in SageMaker Studio, SageMaker notebook instances, or your local machine with AWS CLI set up. If using SageMaker Studio or SageMaker notebook instances, make sure you choose one of the PyTorch-based kernels, Python 3 (PyTorch x.y Python 3.x CPU Optimized) or conda_pytorch_p36 respectively.\n",
     "\n",
-    "**NOTE:** This notebook uses two `ml.p3.2xlarge` instances that have single GPU. If you don't have enough quota, see [Request a service quota increase for SageMaker resources](https://docs.aws.amazon.com/sagemaker/latest/dg/regions-quotas.html#service-limit-increase-request-procedure). "
+    "**NOTE:** This notebook uses two ml.p3.2xlarge instances that have single GPU. If you don't have enough quota, see [Request a service quota increase for SageMaker resources](https://docs.aws.amazon.com/sagemaker/latest/dg/regions-quotas.html#service-limit-increase-request-procedure). "
    ]
   },
   {
@@ -58,7 +58,7 @@
    "source": [
     "### Installation\n",
     "\n",
-    "This example notebook requires the **SageMaker Python SDK v2.70.0** and **transformers v4.11.0**."
+    "This example notebook requires the **SageMaker Python SDK v2.108.0** and **transformers v4.21**."
    ]
   },
   {
@@ -67,7 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install sagemaker botocore boto3 awscli s3fs typing-extensions --upgrade"
+    "!pip install \"sagemaker>=2.108.0\" botocore boto3 awscli s3fs typing-extensions \"torch==1.11.0\" pandas numpy --upgrade"
    ]
   },
   {
@@ -76,7 +76,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install transformers datasets --upgrade"
+    "!pip install \"transformers==4.21\" datasets --upgrade"
    ]
   },
   {
@@ -98,7 +98,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Copy and run the following code if you need to upgrade ipywidgets for `datasets` library and restart kernel. This is only needed when prerpocessing is done in the notebook.\n",
+    "Copy and run the following code if you need to upgrade `ipywidgets` for `datasets` library and restart kernel. This is only needed when preprocessing is done in the notebook.\n",
     "\n",
     "```python\n",
     "%%capture\n",
@@ -120,7 +120,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note:** If you are going to use Sagemaker in a local environment. You need access to an IAM Role with the required permissions for SageMaker. To learn more, see [SageMaker Roles](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html)."
+    "**Note:** If you are going to use SageMaker in a local environment. You need access to an IAM Role with the required permissions for SageMaker. To learn more, see [SageMaker Roles](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html)."
    ]
   },
   {
@@ -322,7 +322,7 @@
     "id": "Vl6IidfdIrJK"
    },
    "source": [
-    "The following assertion ensures that our tokenizer is a fast tokenizers (backed by Rust) from the 🤗 Tokenizers library. Those fast tokenizers are available for almost all models, and we will need some of the special features they have for our preprocessing."
+    "The following assertion ensures that our tokenizer is a fast tokenizer (backed by Rust) from the 🤗 Tokenizers library. Those fast tokenizers are available for almost all models, and we will need some of the special features they have for our preprocessing."
    ]
   },
   {
@@ -515,7 +515,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Before we kick off our SageMaker training job we need to transfer our dataset to S3 so the training job can download it from S3."
+    "Before we kick off our SageMaker training job we need to transfer our dataset to S3, so the training job can download it from S3."
    ]
   },
   {
@@ -563,11 +563,11 @@
    "source": [
     "## SageMaker Training Job\n",
     "\n",
-    "To create a SageMaker training job, we use a `HuggingFace` estimator. Using the estimator, you can define which fine-tuning script should SageMaker use through `entry_point`, which `instance_type` to use for training, which `hyperparameters` to pass, and so on.\n",
+    "To create a SageMaker training job, we use a HuggingFace/PyTorch estimator. Using the estimator, you can define which fine-tuning script should SageMaker use through entry_point, which instance_type to use for training, which hyperparameters to pass, and so on.\n",
     "\n",
-    "When a SageMaker training job starts, SageMaker takes care of starting and managing all the required machine learning instances, picks up the `HuggingFace` Deep Learning Container, uploads your training script, and downloads the data from `sagemaker_session_bucket` into the container at `/opt/ml/input/data`.\n",
+    "When a SageMaker training job starts, SageMaker takes care of starting and managing all the required machine learning instances, picks up the HuggingFace Deep Learning Container, uploads your training script, and downloads the data from sagemaker_session_bucket into the container at /opt/ml/input/data.\n",
     "\n",
-    "In the following section, you learn how to set up two versions of the SageMaker `HuggingFace` estimator, a native one without the compiler and an optimized one with the compiler."
+    "In the following section, you learn how to set up two versions of the SageMaker HuggingFace/PyTorch estimator, a native one without the compiler and an optimized one with the compiler."
    ]
   },
   {
@@ -581,13 +581,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Below, we run a native PyTorch training job with the `HuggingFace` estimator on a `ml.p3.2xlarge` instance. \n",
+    "Below, we run a native PyTorch training job with the PyTorch estimator on a ml.p3.2xlarge instance. \n",
     "\n",
-    "We run a batch size of 28 on our native training job and 52 on our Training Compiler training job to make an apples to apples comparision. These batch sizes along with the max_length variable get us close to 100% GPU memory utilization.\n",
+    "We run a batch size of 28 on our native training job and 52 on our Training Compiler training job to make an apple to apple comparison. These batch sizes along with the max_length variable get us close to 100% GPU memory utilization.\n",
     "\n",
     "We recommend using the tested batch size that's provided at [Tested Models](https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler-support.html#training-compiler-tested-models) in the *SageMaker Training Compiler Developer Guide*.\n",
     "\n",
-    "![gpu mem](images/gpumem.png)"
+    "![`GPU MEM`](images/gpumem.png)"
    ]
   },
   {
@@ -596,7 +596,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.huggingface import HuggingFace\n",
+    "from sagemaker.pytorch import PyTorch\n",
     "\n",
     "batch_size_native = 28\n",
     "learning_rate_native = float(\"3e-5\") / 32 * batch_size_native\n",
@@ -622,15 +622,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "huggingface_estimator = HuggingFace(\n",
+    "native_estimator = PyTorch(\n",
     "    entry_point=\"qa_trainer_huggingface.py\",\n",
     "    source_dir=\"./scripts\",\n",
     "    instance_type=\"ml.p3.2xlarge\",\n",
     "    instance_count=1,\n",
     "    role=role,\n",
     "    py_version=\"py38\",\n",
-    "    transformers_version=\"4.11.0\",\n",
-    "    pytorch_version=\"1.9.0\",\n",
+    "    transformers_version=\"4.21.1\",\n",
+    "    framework_version=\"1.11.0\",\n",
     "    volume_size=volume_size,\n",
     "    hyperparameters=hyperparameters,\n",
     "    disable_profiler=True,\n",
@@ -638,10 +638,10 @@
     ")\n",
     "\n",
     "# starting the train job with our uploaded datasets as input\n",
-    "huggingface_estimator.fit({\"train\": training_input_path, \"test\": eval_input_path}, wait=False)\n",
+    "native_estimator.fit({\"train\": training_input_path, \"test\": eval_input_path}, wait=False)\n",
     "\n",
     "# The name of the training job. You might need to note this down in case you lose connection to your notebook.\n",
-    "huggingface_estimator.latest_training_job.name"
+    "native_estimator.latest_training_job.name"
    ]
   },
   {
@@ -697,7 +697,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "compile_estimator = HuggingFace(\n",
+    "optimized_estimator = HuggingFace(\n",
     "    entry_point=\"qa_trainer_huggingface.py\",\n",
     "    compiler_config=TrainingCompilerConfig(),\n",
     "    source_dir=\"./scripts\",\n",
@@ -705,8 +705,8 @@
     "    instance_count=1,\n",
     "    role=role,\n",
     "    py_version=\"py38\",\n",
-    "    transformers_version=\"4.11.0\",\n",
-    "    pytorch_version=\"1.9.0\",\n",
+    "    transformers_version=\"4.21.1\",\n",
+    "    pytorch_version=\"1.11.0\",\n",
     "    volume_size=volume_size,\n",
     "    hyperparameters=hyperparameters,\n",
     "    disable_profiler=True,\n",
@@ -714,10 +714,10 @@
     ")\n",
     "\n",
     "# starting the train job with our uploaded datasets as input\n",
-    "compile_estimator.fit({\"train\": training_input_path, \"test\": eval_input_path}, wait=False)\n",
+    "optimized_estimator.fit({\"train\": training_input_path, \"test\": eval_input_path}, wait=False)\n",
     "\n",
     "# The name of the training job. You might need to note this down in case you lose connection to your notebook.\n",
-    "compile_estimator.latest_training_job.name"
+    "optimized_estimator.latest_training_job.name"
    ]
   },
   {
@@ -728,14 +728,14 @@
    "source": [
     "# Wait for training jobs to complete.\n",
     "\n",
-    "waiter = huggingface_estimator.sagemaker_session.sagemaker_client.get_waiter(\n",
+    "waiter = native_estimator.sagemaker_session.sagemaker_client.get_waiter(\n",
     "    \"training_job_completed_or_stopped\"\n",
     ")\n",
-    "waiter.wait(TrainingJobName=huggingface_estimator.latest_training_job.name)\n",
-    "waiter = compile_estimator.sagemaker_session.sagemaker_client.get_waiter(\n",
+    "waiter.wait(TrainingJobName=native_estimator.latest_training_job.name)\n",
+    "waiter = optimized_estimator.sagemaker_session.sagemaker_client.get_waiter(\n",
     "    \"training_job_completed_or_stopped\"\n",
     ")\n",
-    "waiter.wait(TrainingJobName=compile_estimator.latest_training_job.name)"
+    "waiter.wait(TrainingJobName=optimized_estimator.latest_training_job.name)"
    ]
   },
   {
@@ -759,14 +759,14 @@
    "outputs": [],
    "source": [
     "# container image used for native training job\n",
-    "print(f\"container image used for training job: \\n{huggingface_estimator.image_uri}\\n\")\n",
+    "print(f\"container image used for training job: \\n{native_estimator.image_uri}\\n\")\n",
     "\n",
     "# s3 uri where the native trained model is located\n",
-    "print(f\"s3 uri where the trained model is located: \\n{huggingface_estimator.model_data}\\n\")\n",
+    "print(f\"s3 uri where the trained model is located: \\n{native_estimator.model_data}\\n\")\n",
     "\n",
     "# latest training job name for this estimator\n",
     "print(\n",
-    "    f\"latest training job name for this estimator: \\n{huggingface_estimator.latest_training_job.name}\\n\"\n",
+    "    f\"latest training job name for this estimator: \\n{native_estimator.latest_training_job.name}\\n\"\n",
     ")"
    ]
   },
@@ -779,16 +779,16 @@
     "%%capture native\n",
     "\n",
     "# access the logs of the native training job\n",
-    "huggingface_estimator.sagemaker_session.logs_for_job(huggingface_estimator.latest_training_job.name)"
+    "native_estimator.sagemaker_session.logs_for_job(native_estimator.latest_training_job.name)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note:** If the estimator object is no longer available due to a kernel break or refresh, you need to directly use the training job name and manually attach the training job to a new HuggingFace estimator. For example:\n",
+    "**Note:** If the estimator object is no longer available due to a kernel break or refresh, you need to directly use the training job name and manually attach the training job to a new PyTorch estimator. For example:\n",
     "```python\n",
-    "huggingface_estimator = HuggingFace.attach(\"your_huggingface_training_job_name\")\n",
+    "native_estimator = PyTorch.attach(\"your_native_training_job_name\")\n",
     "```"
    ]
   },
@@ -806,14 +806,14 @@
    "outputs": [],
    "source": [
     "# container image used for optimized training job\n",
-    "print(f\"container image used for training job: \\n{compile_estimator.image_uri}\\n\")\n",
+    "print(f\"container image used for training job: \\n{optimized_estimator.image_uri}\\n\")\n",
     "\n",
     "# s3 uri where the optimized trained model is located\n",
-    "print(f\"s3 uri where the trained model is located: \\n{compile_estimator.model_data}\\n\")\n",
+    "print(f\"s3 uri where the trained model is located: \\n{optimized_estimator.model_data}\\n\")\n",
     "\n",
     "# latest training job name for this estimator\n",
     "print(\n",
-    "    f\"latest training job name for this estimator: \\n{compile_estimator.latest_training_job.name}\\n\"\n",
+    "    f\"latest training job name for this estimator: \\n{optimized_estimator.latest_training_job.name}\\n\"\n",
     ")"
    ]
   },
@@ -826,7 +826,7 @@
     "%%capture optimized\n",
     "\n",
     "# access the logs of the optimized training job\n",
-    "compile_estimator.sagemaker_session.logs_for_job(compile_estimator.latest_training_job.name)"
+    "optimized_estimator.sagemaker_session.logs_for_job(optimized_estimator.latest_training_job.name)"
    ]
   },
   {
@@ -835,7 +835,7 @@
    "source": [
     "**Note:** If the estimator object is no longer available due to a kernel break or refresh, you need to directly use the training job name and manually attach the training job to a new HuggingFace estimator. For example:\n",
     "```python\n",
-    "optimized_est = HuggingFace.attach(\"your_compiled_huggingface_training_job_name\")\n",
+    "optimized_est = HuggingFace.attach(\"your_optimized_native_training_job_name\")\n",
     "```"
    ]
   },
@@ -925,16 +925,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![throughput](images/throughput.png)"
+    "### Training Stats\n",
+    "Let's compare various training metrics with and without SageMaker Training Compiler. SageMaker Training Compiler provides an increase in training throughput which translates to a decrease in total training time."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### Training Time Plot\n",
+    "import pandas as pd\n",
     "\n",
-    "The following script creates a plot that compares the billable time of the two training jobs with and without SageMaker Training Compiler."
+    "pd.DataFrame([n[\"summary\"], o[\"summary\"]], index=[\"Native\", \"Optimized\"])"
    ]
   },
   {
@@ -943,34 +946,60 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sm = boto3.client(\"sagemaker\")\n",
-    "native_job = sm.describe_training_job(\n",
-    "    TrainingJobName=huggingface_estimator.latest_training_job.name\n",
+    "# calculate percentage speedup from SageMaker Training Compiler in terms of total training time reported by HF\n",
+    "speedup = (\n",
+    "    (n[\"summary\"][\"train_runtime\"] - o[\"summary\"][\"train_runtime\"])\n",
+    "    * 100\n",
+    "    / n[\"summary\"][\"train_runtime\"]\n",
     ")\n",
-    "\n",
-    "compile_job = sm.describe_training_job(TrainingJobName=compile_estimator.latest_training_job.name)\n",
-    "\n",
-    "n_time = native_job[\"BillableTimeInSeconds\"]\n",
-    "h_time = compile_job[\"BillableTimeInSeconds\"]\n",
-    "\n",
-    "time_decrease = round(((n_time - h_time) / n_time) * 100, 2)\n",
-    "\n",
-    "plt.title(\"Training Time\")\n",
-    "plt.ylabel(\"Minutes\")\n",
-    "\n",
-    "plt.bar(x=[1], height=n_time / 60, label=\"Baseline PT\", width=0.35)\n",
-    "plt.bar(x=[1.5], height=h_time / 60, label=\"Training Compiler PT\", width=0.35)\n",
-    "\n",
-    "plt.xlabel(f\"====>  {time_decrease}% faster  <====\")\n",
-    "plt.xticks(ticks=[1, 1.5], labels=[\"Baseline PT\", \"Training Compiler PT\"])\n",
-    "plt.show()"
+    "print(\n",
+    "    f\"SageMaker Training Compiler integrated PyTorch is about {int(speedup)}% faster in terms of total training time as reported by HF.\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![training time](images/trainingtime.png)"
+    "### Billable Time\n",
+    "\n",
+    "The following script creates a plot that compares the billable time of the two training jobs with and without SageMaker Training Compiler."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def BillableTimeInSeconds(name):\n",
+    "    describe_training_job = (\n",
+    "        optimized_estimator.sagemaker_session.sagemaker_client.describe_training_job\n",
+    "    )\n",
+    "    details = describe_training_job(TrainingJobName=name)\n",
+    "    return details[\"BillableTimeInSeconds\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Billable = {}\n",
+    "Billable[\"Native\"] = BillableTimeInSeconds(native_estimator.latest_training_job.name)\n",
+    "Billable[\"Optimized\"] = BillableTimeInSeconds(optimized_estimator.latest_training_job.name)\n",
+    "pd.DataFrame(Billable, index=[\"BillableSecs\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "speedup = (Billable[\"Native\"] - Billable[\"Optimized\"]) * 100 / Billable[\"Native\"]\n",
+    "print(f\"SageMaker Training Compiler integrated PyTorch was {int(speedup)}% faster in summary.\")"
    ]
   },
   {
@@ -1002,20 +1031,13 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![loss](images/loss.png)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Conclusion\n",
     "\n",
-    "In this example, we fine-tuned an [ALBERT model](https://huggingface.co/albert-base-v2) (`albert-base-v2`) with the SQuAD dataset and compared a native training job with a SageMaker Training Compiler training job. The Training Compiler job has `86% higher throughput` and `40% quicker training` time while training loss was equal with the native pytorch training job."
+    "In this example, we fine-tuned an [ALBERT model](https://huggingface.co/albert-base-v2) (albert-base-v2) with the SQuAD dataset and compared a native training job with a SageMaker Training Compiler training job. The Training Compiler job has 93% higher throughput and 38% quicker training time while training loss was equal with the native PyTorch training job."
    ]
   },
   {
@@ -1051,8 +1073,8 @@
     "        sm.stop_training_job(TrainingJobName=name)\n",
     "\n",
     "\n",
-    "stop_training_job(huggingface_estimator.latest_training_job.name)\n",
-    "stop_training_job(compile_estimator.latest_training_job.name)"
+    "stop_training_job(native_estimator.latest_training_job.name)\n",
+    "stop_training_job(optimized_estimator.latest_training_job.name)"
    ]
   },
   {
@@ -1070,9 +1092,9 @@
   },
   "instance_type": "ml.p3.2xlarge",
   "kernelspec": {
-   "display_name": "conda_pytorch_p36",
+   "display_name": "conda_pytorch_p38",
    "language": "python",
-   "name": "conda_pytorch_p36"
+   "name": "conda_pytorch_p38"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1084,7 +1106,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/albert-base-v2/scripts/requirements.txt b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/albert-base-v2/scripts/requirements.txt
new file mode 100644
index 0000000000..db6140254d
--- /dev/null
+++ b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/albert-base-v2/scripts/requirements.txt
@@ -0,0 +1,2 @@
+transformers == 4.21.1
+datasets == 1.18.4
\ No newline at end of file
diff --git a/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/bert-base-cased/bert-base-cased-single-node-single-gpu.ipynb b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/bert-base-cased/bert-base-cased-single-node-single-gpu.ipynb
index 1650577653..2de9b89d3c 100644
--- a/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/bert-base-cased/bert-base-cased-single-node-single-gpu.ipynb
+++ b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/bert-base-cased/bert-base-cased-single-node-single-gpu.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Compile and Train a Hugging Face Transformer ``BERT`` Model with the SST Dataset using SageMaker Training Compiler"
+    "# Compile and Train a Hugging Face Transformer BERT Model with the SST Dataset using SageMaker Training Compiler"
    ]
   },
   {
@@ -50,13 +50,13 @@
    "source": [
     "## Introduction\n",
     "\n",
-    "This notebooks is an end-to-end binary text classification example. In this demo, we use the Hugging Face's `transformers` and `datasets` libraries with SageMaker Training Compiler to compile and fine-tune a pre-trained transformer for binary text classification. In particular, the pre-trained model will be fine-tuned using the Stanford Sentiment Treebank (SST) dataset. To get started, you need to set up the environment with a few prerequisite steps, for permissions, configurations, and so on. \n",
+    "This notebook is an end-to-end binary text classification example. In this demo, we use the Hugging Face's transformers and datasets libraries with SageMaker Training Compiler to compile and fine-tune a pre-trained transformer for binary text classification. In particular, the pre-trained model will be fine-tuned using the Stanford Sentiment Treebank (SST) dataset. To get started, you need to set up the environment with a few prerequisite steps, for permissions, configurations, and so on. \n",
     "\n",
     "![image.png](attachment:image.png)\n",
     "\n",
-    "**NOTE:** You can run this demo in SageMaker Studio, SageMaker notebook instances, or your local machine with AWS CLI set up. If using SageMaker Studio or SageMaker notebook instances, make sure you choose one of the PyTorch-based kernels, `Python 3 (PyTorch x.y Python 3.x CPU Optimized)` or `conda_pytorch_p36` respectively.\n",
+    "**NOTE:** You can run this demo in SageMaker Studio, SageMaker notebook instances, or your local machine with AWS CLI set up. If using SageMaker Studio or SageMaker notebook instances, make sure you choose one of the PyTorch-based kernels, Python 3 (PyTorch x.y Python 3.x CPU Optimized) or conda_pytorch_p36 respectively.\n",
     "\n",
-    "**NOTE:** This notebook uses two `ml.p3.2xlarge` instances that have single GPU. If you don't have enough quota, see [Request a service quota increase for SageMaker resources](https://docs.aws.amazon.com/sagemaker/latest/dg/regions-quotas.html#service-limit-increase-request-procedure). "
+    "**NOTE:** This notebook uses two ml.p3.2xlarge instances that have single GPU. If you don't have enough quota, see [Request a service quota increase for SageMaker resources](https://docs.aws.amazon.com/sagemaker/latest/dg/regions-quotas.html#service-limit-increase-request-procedure). "
    ]
   },
   {
@@ -72,7 +72,7 @@
    "source": [
     "### Installation\n",
     "\n",
-    "This example notebook requires the **SageMaker Python SDK v2.70.0** and **transformers v4.11.0**."
+    "This example notebook requires the **SageMaker Python SDK v2.108.0** and **transformers v4.21**."
    ]
   },
   {
@@ -81,7 +81,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install sagemaker botocore boto3 awscli s3fs typing-extensions --upgrade"
+    "!pip install \"sagemaker>=2.108.0\" botocore boto3 awscli s3fs typing-extensions \"torch==1.11.0\" --upgrade"
    ]
   },
   {
@@ -90,7 +90,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install transformers datasets --upgrade"
+    "!pip install \"transformers==4.21\" datasets --upgrade"
    ]
   },
   {
@@ -112,7 +112,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Copy and run the following code if you need to upgrade ipywidgets for `datasets` library and restart kernel. This is only needed when preprocessing is done in the notebook.\n",
+    "Copy and run the following code if you need to upgrade ipywidgets for datasets library and restart kernel. This is only needed when preprocessing is done in the notebook.\n",
     "\n",
     "```python\n",
     "%%capture\n",
@@ -134,7 +134,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note:** If you are going to use Sagemaker in a local environment. You need access to an IAM Role with the required permissions for SageMaker. To learn more, see [SageMaker Roles](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html)."
+    "**Note:** If you are going to use SageMaker in a local environment. You need access to an IAM Role with the required permissions for SageMaker. To learn more, see [SageMaker Roles](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html)."
    ]
   },
   {
@@ -343,7 +343,7 @@
    "source": [
     "### Uploading data to `sagemaker_session_bucket`\n",
     "\n",
-    "After we processed the `datasets` we are going to use the new `FileSystem` [integration](https://huggingface.co/docs/datasets/filesystems.html) to upload our dataset to S3."
+    "After we processed the datasets we are going to use the new FileSystem [integration](https://huggingface.co/docs/datasets/filesystems.html) to upload our dataset to S3."
    ]
   },
   {
@@ -375,11 +375,11 @@
    "source": [
     "## SageMaker Training Job\n",
     "\n",
-    "To create a SageMaker training job, we use a `HuggingFace` estimator. Using the estimator, you can define which fine-tuning script should SageMaker use through `entry_point`, which `instance_type` to use for training, which `hyperparameters` to pass, and so on.\n",
+    "To create a SageMaker training job, we use a HuggingFace/PyTorch estimator. Using the estimator, you can define which fine-tuning script should SageMaker use through entry_point, which instance_type to use for training, which hyperparameters to pass, and so on.\n",
     "\n",
-    "When a SageMaker training job starts, SageMaker takes care of starting and managing all the required machine learning instances, picks up the `HuggingFace` Deep Learning Container, uploads your training script, and downloads the data from `sagemaker_session_bucket` into the container at `/opt/ml/input/data`.\n",
+    "When a SageMaker training job starts, SageMaker takes care of starting and managing all the required machine learning instances, picks up the HuggingFace Deep Learning Container, uploads your training script, and downloads the data from sagemaker_session_bucket into the container at /opt/ml/input/data.\n",
     "\n",
-    "In the following section, you learn how to set up two versions of the SageMaker `HuggingFace` estimator, a native one without the compiler and an optimized one with the compiler."
+    "In the following section, you learn how to set up two versions of the SageMaker HuggingFace/PyTorch estimator, a native one without the compiler and an optimized one with the compiler."
    ]
   },
   {
@@ -404,9 +404,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.huggingface import HuggingFace\n",
+    "from sagemaker.pytorch import PyTorch\n",
     "\n",
-    "hyperparameters = {\"epochs\": 5, \"train_batch_size\": 14, \"model_name\": \"bert-base-cased\"}\n",
+    "hyperparameters = {\"epochs\": 5, \"train_batch_size\": 16, \"model_name\": \"bert-base-cased\"}\n",
     "\n",
     "# Scale the learning rate by batch size, as original LR was using batch size of 32\n",
     "hyperparameters[\"learning_rate\"] = float(\"5e-5\") / 32 * hyperparameters[\"train_batch_size\"]\n",
@@ -421,11 +421,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# By setting the hyperparameters in the HuggingFace Estimator below\n",
+    "# By setting the hyperparameters in the PyTorch Estimator below\n",
     "# and using the AutoModelForSequenceClassification class in the train.py script\n",
     "# we can fine-tune the bert-base-cased pretrained Transformer for sequence classification\n",
     "\n",
-    "huggingface_estimator = HuggingFace(\n",
+    "native_estimator = PyTorch(\n",
     "    entry_point=\"train.py\",\n",
     "    source_dir=\"./scripts\",\n",
     "    instance_type=\"ml.p3.2xlarge\",\n",
@@ -434,18 +434,18 @@
     "    py_version=\"py38\",\n",
     "    base_job_name=\"native-sst-bert-base-cased-p3-2x-pytorch-190\",\n",
     "    volume_size=volume_size,\n",
-    "    transformers_version=\"4.11.0\",\n",
-    "    pytorch_version=\"1.9.0\",\n",
+    "    transformers_version=\"4.21.1\",\n",
+    "    framework_version=\"1.11.0\",\n",
     "    hyperparameters=hyperparameters,\n",
     "    disable_profiler=True,\n",
     "    debugger_hook_config=False,\n",
     ")\n",
     "\n",
     "# starting the train job with our uploaded datasets as input\n",
-    "huggingface_estimator.fit({\"train\": training_input_path, \"test\": test_input_path}, wait=False)\n",
+    "native_estimator.fit({\"train\": training_input_path, \"test\": test_input_path}, wait=False)\n",
     "\n",
     "# The name of the training job. You might need to note this down in case your kernel crashes.\n",
-    "huggingface_estimator.latest_training_job.name"
+    "native_estimator.latest_training_job.name"
    ]
   },
   {
@@ -493,7 +493,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# By setting the hyperparameters in the HuggingFace Estimator below\n",
+    "# By setting the hyperparameters in the PyTorch Estimator below\n",
     "# and using the AutoModelForSequenceClassification class in the train.py script\n",
     "# the bert-base-cased pretrained Transformer is fine-tuned for sequence classification\n",
     "\n",
@@ -508,8 +508,8 @@
     "    py_version=\"py38\",\n",
     "    base_job_name=\"sm-compiled-sst-bert-base-cased-p3-2x-pytorch-190\",\n",
     "    volume_size=volume_size,\n",
-    "    transformers_version=\"4.11.0\",\n",
-    "    pytorch_version=\"1.9.0\",\n",
+    "    transformers_version=\"4.21.1\",\n",
+    "    pytorch_version=\"1.11.0\",\n",
     "    compiler_config=TrainingCompilerConfig(),\n",
     "    hyperparameters=hyperparameters,\n",
     "    disable_profiler=True,\n",
@@ -538,10 +538,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "waiter = huggingface_estimator.sagemaker_session.sagemaker_client.get_waiter(\n",
+    "waiter = native_estimator.sagemaker_session.sagemaker_client.get_waiter(\n",
     "    \"training_job_completed_or_stopped\"\n",
     ")\n",
-    "waiter.wait(TrainingJobName=huggingface_estimator.latest_training_job.name)\n",
+    "waiter.wait(TrainingJobName=native_estimator.latest_training_job.name)\n",
     "waiter = sm_training_compiler_estimator.sagemaker_session.sagemaker_client.get_waiter(\n",
     "    \"training_job_completed_or_stopped\"\n",
     ")\n",
@@ -569,14 +569,14 @@
    "outputs": [],
    "source": [
     "# container image used for native training job\n",
-    "print(f\"container image used for training job: \\n{huggingface_estimator.image_uri}\\n\")\n",
+    "print(f\"container image used for training job: \\n{native_estimator.image_uri}\\n\")\n",
     "\n",
     "# s3 uri where the native trained model is located\n",
-    "print(f\"s3 uri where the trained model is located: \\n{huggingface_estimator.model_data}\\n\")\n",
+    "print(f\"s3 uri where the trained model is located: \\n{native_estimator.model_data}\\n\")\n",
     "\n",
     "# latest training job name for this estimator\n",
     "print(\n",
-    "    f\"latest training job name for this estimator: \\n{huggingface_estimator.latest_training_job.name}\\n\"\n",
+    "    f\"latest training job name for this estimator: \\n{native_estimator.latest_training_job.name}\\n\"\n",
     ")"
    ]
   },
@@ -591,16 +591,16 @@
     "%%capture native\n",
     "\n",
     "# access the logs of the native training job\n",
-    "huggingface_estimator.sagemaker_session.logs_for_job(huggingface_estimator.latest_training_job.name)"
+    "native_estimator.sagemaker_session.logs_for_job(native_estimator.latest_training_job.name)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note:** If the estimator object is no longer available due to a kernel break or refresh, you need to directly use the training job name and manually attach the training job to a new HuggingFace estimator. For example:\n",
+    "**Note:** If the estimator object is no longer available due to a kernel break or refresh, you need to directly use the training job name and manually attach the training job to a new PyTorch estimator. For example:\n",
     "```python\n",
-    "huggingface_estimator = HuggingFace.attach(\"your_huggingface_training_job_name\")\n",
+    "native_estimator = PyTorch.attach(\"your_native_training_job_name\")\n",
     "```"
    ]
   },
@@ -712,7 +712,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Plot and compare throughputs of compiled training and native training"
+    "### Plot and compare throughput of compiled training and native training"
    ]
   },
   {
@@ -765,7 +765,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Example output for SageMaker Training Compiler traing job\n",
+    "#### Example output for SageMaker Training Compiler training job\n",
     "\n",
     "{'train_runtime': 3742.9028,\n",
     " 'train_samples_per_second': 89.969,\n",
@@ -801,27 +801,6 @@
     "plt.xticks(ticks=[1, 1.5], labels=[\"Baseline PT\", \"SM-Training-Compiler-enhanced PT\"])"
    ]
   },
-  {
-   "attachments": {
-    "throughput.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training Throughput Example Plot\n",
-    "\n",
-    "![throughput.png](attachment:throughput.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Note:** For this example, the compiler delivers higher throughput for an ML model as measured by samples per second. However, you might not see an improvement in the total training time for your model. The total training time depends on several other factors, such as key components of the Trainer and TFTrainer APIs."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -916,7 +895,7 @@
     "        sm.stop_training_job(TrainingJobName=name)\n",
     "\n",
     "\n",
-    "stop_training_job(huggingface_estimator.latest_training_job.name)\n",
+    "stop_training_job(native_estimator.latest_training_job.name)\n",
     "stop_training_job(sm_training_compiler_estimator.latest_training_job.name)"
    ]
   },
@@ -933,9 +912,9 @@
    "hash": "c281c456f1b8161c8906f4af2c08ed2c40c50136979eaae69688b01f70e9f4a9"
   },
   "kernelspec": {
-   "display_name": "conda_pytorch_p36",
+   "display_name": "conda_pytorch_p38",
    "language": "python",
-   "name": "conda_pytorch_p36"
+   "name": "conda_pytorch_p38"
   },
   "language_info": {
    "codemirror_mode": {
@@ -947,7 +926,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/bert-base-cased/scripts/requirements.txt b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/bert-base-cased/scripts/requirements.txt
new file mode 100644
index 0000000000..db6140254d
--- /dev/null
+++ b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/bert-base-cased/scripts/requirements.txt
@@ -0,0 +1,2 @@
+transformers == 4.21.1
+datasets == 1.18.4
\ No newline at end of file
diff --git a/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/roberta-base/roberta-base.ipynb b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/roberta-base/roberta-base.ipynb
index 5a41876fca..7e26318a9c 100644
--- a/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/roberta-base/roberta-base.ipynb
+++ b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/roberta-base/roberta-base.ipynb
@@ -38,11 +38,11 @@
     "\n",
     "## Introduction\n",
     "\n",
-    "In this demo, you'll use Hugging Face's `transformers` and `datasets` libraries with Amazon SageMaker Training Compiler to train the `RoBERTa` model on the `Stanford Sentiment Treebank v2 (SST2)` dataset. To get started, we need to set up the environment with a few prerequisite steps, for permissions, configurations, and so on. \n",
+    "In this demo, you'll use Hugging Face's transformers and datasets libraries with Amazon SageMaker Training Compiler to train the RoBERTa model on the Stanford Sentiment Treebank v2 (SST2) dataset. To get started, we need to set up the environment with a few prerequisite steps, for permissions, configurations, and so on. \n",
     "\n",
-    "**NOTE:** You can run this demo in SageMaker Studio, SageMaker notebook instances, or your local machine with AWS CLI set up. If using SageMaker Studio or SageMaker notebook instances, make sure you choose one of the PyTorch-based kernels, `Python 3 (PyTorch x.y Python 3.x CPU Optimized)` or `conda_pytorch_p36` respectively.\n",
+    "**NOTE:** You can run this demo in SageMaker Studio, SageMaker notebook instances, or your local machine with AWS CLI set up. If using SageMaker Studio or SageMaker notebook instances, make sure you choose one of the PyTorch-based kernels, Python 3 (PyTorch x.y Python 3.x CPU Optimized) or conda_pytorch_p36 respectively.\n",
     "\n",
-    "**NOTE:** This notebook uses two `ml.p3.2xlarge` instances that have single GPU. If you don't have enough quota, see [Request a service quota increase for SageMaker resources](https://docs.aws.amazon.com/sagemaker/latest/dg/regions-quotas.html#service-limit-increase-request-procedure). "
+    "**NOTE:** This notebook uses two ml.p3.2xlarge instances that have single GPU. If you don't have enough quota, see [Request a service quota increase for SageMaker resources](https://docs.aws.amazon.com/sagemaker/latest/dg/regions-quotas.html#service-limit-increase-request-procedure). "
    ]
   },
   {
@@ -58,7 +58,7 @@
    "source": [
     "### Installation\n",
     "\n",
-    "This example notebook requires the **SageMaker Python SDK v2.70.0** and **transformers v4.11.0**."
+    "This example notebook requires the **SageMaker Python SDK v2.108.0** and **transformers v4.21**."
    ]
   },
   {
@@ -67,7 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install sagemaker botocore boto3 awscli --upgrade"
+    "!pip install \"sagemaker>=2.108.0\" botocore boto3 awscli \"torch==1.11.0\" --upgrade"
    ]
   },
   {
@@ -76,7 +76,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -U transformers datasets --upgrade"
+    "!pip install -U \"transformers==4.21.1\" datasets --upgrade"
    ]
   },
   {
@@ -99,7 +99,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Copy and run the following code if you need to upgrade ipywidgets for `datasets` library and restart kernel. This is only needed when prerpocessing is done in the notebook.\n",
+    "Copy and run the following code if you need to upgrade ipywidgets for datasets library and restart kernel. This is only needed when preprocessing is done in the notebook.\n",
     "\n",
     "```python\n",
     "%%capture\n",
@@ -164,7 +164,7 @@
     "\n",
     "If you'd like to try other training datasets later, you can simply use this method.\n",
     "\n",
-    "For this example notebook, we prepared the `SST2` dataset in the public SageMaker sample file S3 bucket. The following code cells show how you can directly load the dataset and convert to a HuggingFace DatasetDict."
+    "For this example notebook, we prepared the SST2 dataset in the public SageMaker sample file S3 bucket. The following code cells show how you can directly load the dataset and convert to a HuggingFace DatasetDict."
    ]
   },
   {
@@ -173,7 +173,7 @@
    "source": [
     "## Preprocessing\n",
     "\n",
-    "We download and preprocess the `SST2` dataset from the `s3://sagemaker-sample-files/datasets` bucket. After preprocessing, we'll upload the dataset to the `sagemaker_session_bucket`, which will be used as a data channel for the training job."
+    "We download and preprocess the SST2 dataset from the s3://sagemaker-sample-files/datasets bucket. After preprocessing, we'll upload the dataset to the sagemaker_session_bucket, which will be used as a data channel for the training job."
    ]
   },
   {
@@ -253,9 +253,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Uploading data to `sagemaker_session_bucket`\n",
+    "## Uploading data to sagemaker_session_bucket\n",
     "\n",
-    "We are going to use the new `FileSystem` [integration](https://huggingface.co/docs/datasets/filesystems.html) to upload our preprocessed dataset to S3."
+    "We are going to use the new FileSystem [integration](https://huggingface.co/docs/datasets/filesystems.html) to upload our preprocessed dataset to S3."
    ]
   },
   {
@@ -284,11 +284,11 @@
    "source": [
     "## SageMaker Training Job\n",
     "\n",
-    "To create a SageMaker training job, we use a `HuggingFace` estimator. Using the estimator, you can define which fine-tuning script should SageMaker use through `entry_point`, which `instance_type` to use for training, which `hyperparameters` to pass, and so on.\n",
+    "To create a SageMaker training job, we use a HuggingFace/PyTorch estimator. Using the estimator, you can define which fine-tuning script should SageMaker use through entry_point, which instance_type to use for training, which hyperparameters to pass, and so on.\n",
     "\n",
-    "When a SageMaker training job starts, SageMaker takes care of starting and managing all the required machine learning instances, picks up the `HuggingFace` Deep Learning Container, uploads your training script, and downloads the data from `sagemaker_session_bucket` into the container at `/opt/ml/input/data`.\n",
+    "When a SageMaker training job starts, SageMaker takes care of starting and managing all the required machine learning instances, picks up the HuggingFace Deep Learning Container, uploads your training script, and downloads the data from sagemaker_session_bucket into the container at /opt/ml/input/data.\n",
     "\n",
-    "In the following section, you learn how to set up two versions of the SageMaker `HuggingFace` estimator, a native one without the compiler and an optimized one with the compiler."
+    "In the following section, you learn how to set up two versions of the SageMaker HuggingFace/PyTorch estimator, a native one without the compiler and an optimized one with the compiler."
    ]
   },
   {
@@ -302,7 +302,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Set up an option for fine-tuning or full training. Set `FINE_TUNING = 1` for fine-tuning and using `fine_tune_with_huggingface.py`. Set `FINE_TUNING = 0` for full training and using `full_train_roberta_with_huggingface.py`."
+    "Set up an option for fine-tuning or full training. `FINE_TUNING = 1` is for fine-tuning, and it will use fine_tune_with_huggingface.py. `FINE_TUNING = 0` is for full training, and it will use full_train_roberta_with_huggingface.py."
    ]
   },
   {
@@ -318,7 +318,7 @@
     "FULL_TRAINING = not FINE_TUNING\n",
     "\n",
     "# Fine tuning is typically faster and is done for fewer epochs\n",
-    "EPOCHS = 4 if FINE_TUNING else 100\n",
+    "EPOCHS = 7 if FINE_TUNING else 100\n",
     "\n",
     "TRAINING_SCRIPT = (\n",
     "    \"fine_tune_with_huggingface.py\" if FINE_TUNING else \"full_train_roberta_with_huggingface.py\"\n",
@@ -340,7 +340,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `train_batch_size` in the following code cell is the maximum batch that can fit into the memory of an `ml.p3.2xlarge` instance. If you change the model, instance type, sequence length, and other parameters, you need to do some experiments to find the largest batch size that will fit into GPU memory."
+    "The `train_batch_size` in the following code cell is the maximum batch that can fit into the memory of the ml.p3.2xlarge instance. If you change the model, instance type, sequence length, and other parameters, you need to do some experiments to find the largest batch size that will fit into GPU memory."
    ]
   },
   {
@@ -349,7 +349,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.huggingface import HuggingFace, TrainingCompilerConfig\n",
+    "from sagemaker.pytorch import PyTorch\n",
     "\n",
     "# hyperparameters, which are passed into the training job\n",
     "hyperparameters = {\"epochs\": EPOCHS, \"train_batch_size\": 18, \"model_name\": \"roberta-base\"}\n",
@@ -367,15 +367,15 @@
    "outputs": [],
    "source": [
     "# configure the training job\n",
-    "huggingface_estimator = HuggingFace(\n",
+    "native_estimator = PyTorch(\n",
     "    entry_point=TRAINING_SCRIPT,\n",
     "    source_dir=\"./scripts\",\n",
     "    instance_type=INSTANCE_TYPE,\n",
     "    instance_count=1,\n",
     "    role=role,\n",
     "    py_version=\"py38\",\n",
-    "    transformers_version=\"4.11.0\",\n",
-    "    pytorch_version=\"1.9.0\",\n",
+    "    transformers_version=\"4.21.1\",\n",
+    "    framework_version=\"1.11.0\",\n",
     "    volume_size=volume_size,\n",
     "    hyperparameters=hyperparameters,\n",
     "    disable_profiler=True,\n",
@@ -383,10 +383,10 @@
     ")\n",
     "\n",
     "# start training with our uploaded datasets as input\n",
-    "huggingface_estimator.fit({\"train\": training_input_path, \"test\": test_input_path}, wait=False)\n",
+    "native_estimator.fit({\"train\": training_input_path, \"test\": test_input_path}, wait=False)\n",
     "\n",
     "# The name of the training job.\n",
-    "huggingface_estimator.latest_training_job.name"
+    "native_estimator.latest_training_job.name"
    ]
   },
   {
@@ -417,7 +417,9 @@
     "hyperparameters[\"learning_rate\"] = float(\"5e-5\") / 32 * hyperparameters[\"train_batch_size\"]\n",
     "\n",
     "# If checkpointing is enabled with higher epoch numbers, your disk requirements will increase as well\n",
-    "volume_size = 60 + 2 * hyperparameters[\"epochs\"]"
+    "volume_size = 60 + 2 * hyperparameters[\"epochs\"]\n",
+    "\n",
+    "from sagemaker.huggingface import HuggingFace, TrainingCompilerConfig"
    ]
   },
   {
@@ -435,8 +437,8 @@
     "    instance_count=1,\n",
     "    role=role,\n",
     "    py_version=\"py38\",\n",
-    "    transformers_version=\"4.11.0\",\n",
-    "    pytorch_version=\"1.9.0\",\n",
+    "    transformers_version=\"4.21.1\",\n",
+    "    pytorch_version=\"1.11.0\",\n",
     "    volume_size=volume_size,\n",
     "    hyperparameters=hyperparameters,\n",
     "    disable_profiler=True,\n",
@@ -463,10 +465,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "waiter = huggingface_estimator.sagemaker_session.sagemaker_client.get_waiter(\n",
+    "waiter = native_estimator.sagemaker_session.sagemaker_client.get_waiter(\n",
     "    \"training_job_completed_or_stopped\"\n",
     ")\n",
-    "waiter.wait(TrainingJobName=huggingface_estimator.latest_training_job.name)\n",
+    "waiter.wait(TrainingJobName=native_estimator.latest_training_job.name)\n",
     "waiter = optimized_estimator.sagemaker_session.sagemaker_client.get_waiter(\n",
     "    \"training_job_completed_or_stopped\"\n",
     ")\n",
@@ -494,14 +496,14 @@
    "outputs": [],
    "source": [
     "# container image used for native training job\n",
-    "print(f\"container image used for training job: \\n{huggingface_estimator.image_uri}\\n\")\n",
+    "print(f\"container image used for training job: \\n{native_estimator.image_uri}\\n\")\n",
     "\n",
     "# s3 uri where the native trained model is located\n",
-    "print(f\"s3 uri where the trained model is located: \\n{huggingface_estimator.model_data}\\n\")\n",
+    "print(f\"s3 uri where the trained model is located: \\n{native_estimator.model_data}\\n\")\n",
     "\n",
     "# latest training job name for this estimator\n",
     "print(\n",
-    "    f\"latest training job name for this estimator: \\n{huggingface_estimator.latest_training_job.name}\\n\"\n",
+    "    f\"latest training job name for this estimator: \\n{native_estimator.latest_training_job.name}\\n\"\n",
     ")"
    ]
   },
@@ -514,16 +516,16 @@
     "%%capture native\n",
     "\n",
     "# access the logs of the native training job\n",
-    "huggingface_estimator.sagemaker_session.logs_for_job(huggingface_estimator.latest_training_job.name)"
+    "native_estimator.sagemaker_session.logs_for_job(native_estimator.latest_training_job.name)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note:** If the estimator object is no longer available due to a kernel break or refresh, you need to directly use the training job name and manually attach the training job to a new HuggingFace estimator. For example:\n",
+    "**Note:** If the estimator object is no longer available due to a kernel break or refresh, you need to directly use the training job name and manually attach the training job to a new PyTorch estimator. For example:\n",
     "```python\n",
-    "huggingface_estimator = HuggingFace.attach(\"your_huggingface_training_job_name\")\n",
+    "native_estimator = PyTorch.attach(\"your_huggingface_training_job_name\")\n",
     "```"
    ]
   },
@@ -626,9 +628,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Plot and compare throughputs of compiled training and native training\n",
+    "### Plot and compare throughput of compiled training and native training\n",
     "\n",
-    "Visualize average throughputs as reported by HuggingFace and see potential savings."
+    "Visualize average throughput as reported by HuggingFace and see potential savings."
    ]
   },
   {
@@ -782,7 +784,7 @@
    "outputs": [],
    "source": [
     "Billable = {}\n",
-    "Billable[\"Native\"] = BillableTimeInSeconds(huggingface_estimator.latest_training_job.name)\n",
+    "Billable[\"Native\"] = BillableTimeInSeconds(native_estimator.latest_training_job.name)\n",
     "Billable[\"Optimized\"] = BillableTimeInSeconds(optimized_estimator.latest_training_job.name)\n",
     "pd.DataFrame(Billable, index=[\"BillableSecs\"])"
    ]
@@ -823,7 +825,7 @@
     "        sm.stop_training_job(TrainingJobName=name)\n",
     "\n",
     "\n",
-    "stop_training_job(huggingface_estimator.latest_training_job.name)\n",
+    "stop_training_job(native_estimator.latest_training_job.name)\n",
     "stop_training_job(optimized_estimator.latest_training_job.name)"
    ]
   },
@@ -841,9 +843,9 @@
    "hash": "c281c456f1b8161c8906f4af2c08ed2c40c50136979eaae69688b01f70e9f4a9"
   },
   "kernelspec": {
-   "display_name": "conda_pytorch_latest_p36",
+   "display_name": "conda_pytorch_p38",
    "language": "python",
-   "name": "conda_pytorch_latest_p36"
+   "name": "conda_pytorch_p38"
   },
   "language_info": {
    "codemirror_mode": {
@@ -855,7 +857,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/roberta-base/scripts/requirements.txt b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/roberta-base/scripts/requirements.txt
new file mode 100644
index 0000000000..db6140254d
--- /dev/null
+++ b/sagemaker-training-compiler/huggingface/pytorch_single_gpu_single_node/roberta-base/scripts/requirements.txt
@@ -0,0 +1,2 @@
+transformers == 4.21.1
+datasets == 1.18.4
\ No newline at end of file