📄 Update docs for changes #1762453598998

concepts/deployment.mdx

@@ -62,7 +62,7 @@ deployment: !Deployment
   destination: gcp
   endpoint_name: opt-125m-gcp
   instance_count: 1
-  machine_type: n1-standard-4
+  instance_type: n1-standard-4
   accelerator_type: NVIDIA_TESLA_T4
   accelerator_count: 1
 

concepts/fine-tuning.mdx

@@ -5,6 +5,10 @@ description: Guide to fine-tuning models with Magemaker
 
 ## Fine-tuning Overview
 
+<Note>
+Fine-tuning is currently only available for **AWS SageMaker**. Support for GCP and Azure is coming soon.
+</Note>
+
 Fine-tuning allows you to adapt pre-trained models to your specific use case. Magemaker simplifies this process through YAML configuration.
 
 ### Basic Command
@@ -26,10 +30,15 @@ training: !Training
 
 models:
 - !Model
-  id: your-model-id
-  source: huggingface
+  id: tensorflow-tc-bert-en-uncased-L-12-H-768-A-12-2
+  version: 1.0.0
+  source: sagemaker
 ```
 
+<Note>
+Currently, fine-tuning only supports **SageMaker JumpStart models** (source: `sagemaker`). Support for Hugging Face models is not yet implemented.
+</Note>
+
 ### Advanced Configuration
 
 ```yaml
@@ -38,15 +47,17 @@ training: !Training
   instance_type: ml.p3.2xlarge
   instance_count: 1
   training_input_path: s3://your-bucket/data.csv
+  output_path: s3://your-bucket/output
   hyperparameters: !Hyperparameters
     epochs: 3
     per_device_train_batch_size: 32
     learning_rate: 2e-5
-    weight_decay: 0.01
-    warmup_steps: 500
-    evaluation_strategy: "steps"
-    eval_steps: 500
-    save_steps: 1000
+
+models:
+- !Model
+  id: tensorflow-tc-bert-en-uncased-L-12-H-768-A-12-2
+  version: 1.0.0
+  source: sagemaker
 ```
 
 ## Data Preparation
@@ -98,27 +109,20 @@ Popular choices:
 
 ## Hyperparameter Tuning
 
-### Basic Parameters
+### Supported Hyperparameters
+
+The following hyperparameters can be configured (all optional):
 
 ```yaml
 hyperparameters: !Hyperparameters
   epochs: 3
+  per_device_train_batch_size: 32
   learning_rate: 2e-5
-  batch_size: 32
 ```
 
-### Advanced Tuning
-
-```yaml
-hyperparameters: !Hyperparameters
-  epochs: 3
-  learning_rate: 
-    min: 1e-5
-    max: 1e-4
-    scaling: log
-  batch_size:
-    values: [16, 32, 64]
-```
+<Note>
+These hyperparameters override the default values for the SageMaker JumpStart model. Any hyperparameter not specified will use the model's default value.
+</Note>
 
 ## Monitoring Training
 

concepts/models.mdx

@@ -5,12 +5,12 @@ description: Guide to supported models and their requirements
 
 ## Supported Models
 
-<Note>
-Currently, Magemaker supports deployment of Hugging Face models only. Support for cloud provider marketplace models is coming soon!
-</Note>
+Magemaker supports multiple model sources depending on your cloud provider:
 
 ### Hugging Face Models
 
+Hugging Face models can be deployed to **all three cloud providers** (AWS, GCP, Azure):
+
 <CardGroup>
   <Card title="Text Generation" icon="pen-to-square" href="https://huggingface.co/models?pipeline_tag=text-generation">
     - LLaMA
@@ -26,19 +26,63 @@ Currently, Magemaker supports deployment of Hugging Face models only. Support fo
   </Card>
 </CardGroup>
 
+### AWS SageMaker JumpStart Models
+
+<Note>
+SageMaker JumpStart models are available when using the **interactive deployment menu** with `magemaker --cloud aws`.
+</Note>
+
+AWS SageMaker JumpStart provides pre-trained, open-source models from various frameworks:
+
+- **Hugging Face** models
+- **Meta** models (e.g., Llama)
+- **TensorFlow** models
+- **PyTorch** models
+- **MXNet** models
+
+To deploy a SageMaker JumpStart model:
+1. Run `magemaker --cloud aws`
+2. Select "Deploy a model endpoint"
+3. Choose "Deploy a Sagemaker model"
+4. Search and select from available models
+
+Example YAML configuration for SageMaker models:
+```yaml
+deployment: !Deployment
+  destination: aws
+  instance_type: ml.m5.xlarge
+
+models:
+- !Model
+  id: huggingface-tc-bert-large-cased
+  source: sagemaker
+```
+
+### Custom Models
+
+You can deploy your own fine-tuned models (currently AWS only):
+
+```yaml
+deployment: !Deployment
+  destination: aws
+  instance_type: ml.m5.xlarge
+
+models:
+- !Model
+  id: google-bert/bert-base-uncased  # base model
+  source: custom
+  location: s3://your-bucket/model.tar.gz  # or local path
+```
+
 ### Future Support
 
-We plan to add support for the following model sources:
+We plan to add support for:
 
 <CardGroup>
-  <Card title="AWS SageMaker" icon="aws">
-    Models from AWS Marketplace and SageMaker built-in algorithms
-  </Card>
-
   <Card title="GCP Vertex AI" icon="google">
     Models from Vertex AI Model Garden and Foundation Models
   </Card>
-  
+
   <Card title="Azure ML" icon="microsoft">
     Models from Azure ML Model Catalog and Azure OpenAI
   </Card>
@@ -65,17 +109,17 @@ We plan to add support for the following model sources:
 #### GCP Vertex AI
 1. **Small Models** (n1-standard-4)
    ```yaml
-   machine_type: n1-standard-4
+   instance_type: n1-standard-4
    ```
 2. **Medium Models** (n1-standard-8 + GPU)
    ```yaml
-   machine_type: n1-standard-8
+   instance_type: n1-standard-8
    accelerator_type: NVIDIA_TESLA_T4
    accelerator_count: 1
    ```
 3. **Large Models** (a2-highgpu-1g)
    ```yaml
-   machine_type: a2-highgpu-1g
+   instance_type: a2-highgpu-1g
    ```
 
 #### Azure ML

configuration/AWS.mdx

@@ -4,8 +4,6 @@ title: AWS
 
 ### AWS CLI
 
-<Note>To install Azure SDK on MacOS, you need to have the latest OS and you need to use Rosetta terminal. Also, make sure you have the latest version of Xcode tools installed.</Note>
-
 Follow this guide to install the latest AWS CLI
 
 https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html

installation.mdx

@@ -3,11 +3,16 @@ title: Installation
 description: Configure Magemaker for your cloud provider
 ---
 
+## Prerequisites
+
+- **Python 3.11+** is required
+- **Python 3.13 is not supported** due to compatibility issues with Azure SDK
 
 <Note>
-  For Macs, maxOS >= 13.6.6 is required. Apply Silicon devices (M1) must use Rosetta terminal. You can verify, your terminals architecture by running `arch`. It should print `i386` for Rosetta terminal.
+  For Macs, macOS >= 13.6.6 is required. Apple Silicon devices (M1/M2/M3) must use Rosetta terminal. You can verify your terminal's architecture by running `arch`. It should print `i386` for Rosetta terminal.
 </Note>
 
+## Installation
 
 Install via pip:
 
@@ -47,7 +52,7 @@ magemaker --cloud gcp
 ### Azure Configuration
 
 - Follow this detailed guide for setting up Azure credentials:
-  [GCP Setup Guide](/configuration/Azure)
+  [Azure Setup Guide](/configuration/Azure)
 
 
 Once you have your Azure credentials, you can configure Magemaker by running:

mint.json

@@ -64,8 +64,15 @@
       "pages": [
         "concepts/deployment",
         "concepts/models",
+        "concepts/fine-tuning",
         "concepts/contributing"
       ]
+    },
+    {
+      "group": "Reference",
+      "pages": [
+        "reference/yaml-schema"
+      ]
     }
   ],
   "footerSocials": {

quick-start.mdx

@@ -54,20 +54,15 @@ Example YAML for AWS deployment:
 
 ```yaml
 deployment: !Deployment
-  destination: aws 
+  destination: aws
   endpoint_name: facebook-opt-test
   instance_count: 1
   instance_type: ml.m5.xlarge
-  num_gpus: null
-  quantization: null
+
 models:
   - !Model
     id: facebook/opt-125m
-    location: null
-    predict: null
     source: huggingface
-    task: text-generation
-    version: null
 ```
 
 For GCP Vertex AI:
@@ -76,20 +71,15 @@ For GCP Vertex AI:
 deployment: !Deployment
   destination: gcp
   endpoint_name: facebook-opt-test
-  accelerator_count: 1
+  instance_count: 1
   instance_type: g2-standard-12
   accelerator_type: NVIDIA_L4
-  num_gpus: null
-  quantization: null
+  accelerator_count: 1
 
 models:
   - !Model
     id: facebook/opt-125m
-    location: null
-    predict: null
     source: huggingface
-    task: null
-    version: null
 ```
 
 For Azure ML:
@@ -100,14 +90,11 @@ deployment: !Deployment
   endpoint_name: facebook-opt-test
   instance_count: 1
   instance_type: Standard_DS3_v2
+
 models:
   - !Model
     id: facebook--opt-125m
-    location: null
-    predict: null
     source: huggingface
-    task: text-generation
-    version: null
 ```
 <Note>
   The model ids for Azure are different from AWS and GCP. Make sure to use the one provided by Azure in the Azure Model Catalog. 
@@ -131,6 +118,10 @@ models:
 
 ### Model Fine-tuning
 
+<Note>
+Fine-tuning is currently only available for AWS SageMaker with SageMaker JumpStart models.
+</Note>
+
 Fine-tune models using the `train` command:
 
 ```sh
@@ -141,14 +132,21 @@ Example training configuration:
 
 ```yaml
 training: !Training
-  destination: aws # or gcp, azure
-  instance_type: ml.p3.2xlarge # varies by cloud provider
+  destination: aws
+  instance_type: ml.p3.2xlarge
   instance_count: 1
   training_input_path: s3://your-bucket/data.csv
+  output_path: s3://your-bucket/output
   hyperparameters: !Hyperparameters
     epochs: 3
     per_device_train_batch_size: 32
     learning_rate: 2e-5
+
+models:
+- !Model
+  id: tensorflow-tc-bert-en-uncased-L-12-H-768-A-12-2
+  version: 1.0.0
+  source: sagemaker
 ```
 {/* 
 ### Recommended Models