Update docs for DeepSparse Enterprise (#101)

* Update docs for DeepSparse Enterprise

* Remove old files, update path location for deepsparse enterprise

* Update deepsparse and sparsezoo to reflect 3.10 python support

* Update install.mdx

Grammar

* Update src/content/get-started/install/deepsparse.mdx

Co-authored-by: Jeannie Finks <[email protected]>

* Update src/content/get-started/install/deepsparse.mdx

Co-authored-by: Jeannie Finks <[email protected]>

* update deepsparse license messaging

* Update install.mdx

should have no spaces between pip install sparseml[torch,torchvision]

Co-authored-by: Jeannie Finks <[email protected]>
Co-authored-by: rsnm2 <[email protected]>

Author: 3 people

src/content/get-started/install.mdx

@@ -9,22 +9,26 @@ index: 0
 # Installation
 
 The Deep Sparse Platform is made up of core libraries that are available as Python APIs and CLIs.
-All Python APIs and CLIs are installable through pip utilizing [PyPi](https://pypi.org/user/neuralmagic/).
+All Python APIs and CLIs are installed through pip utilizing [PyPI](https://pypi.org/user/neuralmagic/).
 It is recommended to install in a [virtual environment](https://docs.python.org/3/library/venv.html) to encapsulate your local environment.
 
 ## Quick Start
 
 To begin using the Deep Sparse Platform, run the following commands which install standard setups for deployment with [DeepSparse](../../products/deepsparse) and model training/optimization with [SparseML](../../products/sparseml):
 
 ```bash
-pip install deepsparse[server] sparseml[torch, torchvision]
+pip install deepsparse[server] sparseml[torch,torchvision]
 ```
 
-## Installable Packages
+## Package Installations
 
 <LinkCards>
   <LinkCard href="./deepsparse" heading="DeepSparse">
-    Install DeepSparse for performant inference on CPUs.
+    Install the DeepSparse Community Edition for performant inference on CPUs.
+  </LinkCard>
+
+  <LinkCard href="./deepsparse-ent" heading="DeepSparse Enterprise">
+    Install the DeepSparse Enterprise Edition for performant inference on CPUs in production deployments.
   </LinkCard>
 
   <LinkCard href="./sparseml" heading="SparseML">

src/content/get-started/install/deepsparse-ent.mdx

@@ -0,0 +1,46 @@
+---
+title: "DeepSparse Enterprise"
+metaTitle: "DeepSparse Enterprise Installation"
+metaDescription: "Installation instructions for the DeepSparse Engine enabling performant neural network deployments"
+githubURL: "https://github.com/neuralmagic/docs/blob/main/src/content/install/deepsparse.mdx"
+index: 2000
+---
+
+# DeepSparse Enterprise Edition Installation
+
+The [DeepSparse Engine](/products/deepsparse-ent) enables GPU-class performance on CPUs, leveraging sparsity within models to reduce FLOPs and the unique cache hierarchy on CPUs to reduce memory movement.
+The engine accepts models in the open-source [ONNX format](https://onnx.ai/), which are easily created from PyTorch and TensorFlow models.
+
+Currently, DeepSparse is tested on Python 3.7-3.10, ONNX 1.5.0-1.10.1, ONNX opset version 11+ and is [manylinux compliant](https://peps.python.org/pep-0513/).
+It is limited to Linux systems running on x86 CPU architectures.
+
+The DeepSparse Engine is available in two editions:
+1. [**The Community Edition**](/products/deepsparse) is open-source and free for evaluation, research, and non-production use with our [Engine Community License](https://neuralmagic.com/legal/engine-license-agreement/).
+2. [**The Enterprise Edition**](/products/deepsparse-ent) requires a Trial License or [can be fully licensed](https://neuralmagic.com/legal/master-software-license-and-service-agreement/) for production, commercial applications.
+
+## General Install
+
+Use the following command to install with pip:
+
+```bash
+pip install deepsparse-ent
+```
+
+## Server Install
+
+The [DeepSparse Server](/use-cases/deploying-deepsparse/deepsparse-server) allows you to serve models and pipelines through an HTTP interface using the deepsparse.server CLI.
+To install, use the following extra option:
+
+```bash
+pip install deepsparse-ent[server]
+```
+
+## YOLO Install
+
+The [Ultralytics YOLOv5](/use-cases/object-detection/deploying) models require extra dependencies for deployment.
+To use YOLO models, install with the following extra option:
+
+```bash
+pip install deepsparse-ent[yolo]         # just yolo requirements
+pip install deepsparse-ent[yolo,server] # both yolo + server requirements
+```

src/content/get-started/install/deepsparse.mdx

@@ -6,17 +6,21 @@ githubURL: "https://github.com/neuralmagic/docs/blob/main/src/content/install/de
 index: 1000
 ---
 
-# DeepSparse Installation
+# DeepSparse Community Edition Installation
 
 The [DeepSparse Engine](/products/deepsparse) enables GPU-class performance on CPUs, leveraging sparsity within models to reduce FLOPs and the unique cache hierarchy on CPUs to reduce memory movement.
 The engine accepts models in the open-source [ONNX format](https://onnx.ai/), which are easily created from PyTorch and TensorFlow models.
 
-Currently, DeepSparse is tested on Python 3.7-3.9, ONNX 1.5.0-1.10.1, ONNX opset version 11+ and is [manylinux compliant](https://peps.python.org/pep-0513/).
+Currently, DeepSparse is tested on Python 3.7-3.10, ONNX 1.5.0-1.10.1, ONNX opset version 11+ and is [manylinux compliant](https://peps.python.org/pep-0513/).
 It is limited to Linux systems running on x86 CPU architectures.
 
-## General Install
+The DeepSparse Engine is available in two editions:
+1. [**The Community Edition**](/products/deepsparse) is open-source and free for evaluation, research, and non-production use with our [Engine Community License](https://neuralmagic.com/legal/engine-license-agreement/).
+2. [**The Enterprise Edition**](/products/deepsparse-ent) requires a Trial License or [can be fully licensed](https://neuralmagic.com/legal/master-software-license-and-service-agreement/) for production, commercial applications.
 
-Use the following command to install with pip:
+## General Installation
+
+Use the following command to install the Community Edition with pip:
 
 ```bash
 pip install deepsparse

src/content/get-started/install/sparseml.mdx

@@ -3,7 +3,7 @@ title: "SparseML"
 metaTitle: "SparseML Installation"
 metaDescription: "Installation instructions for SparseML neural network optimization, training, and sparsification"
 githubURL: "https://github.com/neuralmagic/docs/blob/main/src/content/install/sparseml.mdx"
-index: 2000
+index: 3000
 ---
 
 # SparseML Installation

src/content/get-started/install/sparsezoo.mdx

@@ -3,7 +3,7 @@ title: "SparseZoo"
 metaTitle: "SparseZoo Installation"
 metaDescription: "Installation instructions for the SparseZoo sparse model repository"
 githubURL: "https://github.com/neuralmagic/docs/blob/main/src/content/install/sparsezoo.mdx"
-index: 3000
+index: 4000
 ---
 
 # SparseZoo Installation
@@ -13,7 +13,7 @@ This installs the Python API and CLIs for downloading models and recipes from th
 
 Note that the SparseZoo package is automatically installed with both SparseML and DeepSparse.
 
-Currently, the SparseZoo Python APIs and CLIs are tested on Python 3.7-3.9 and are limited to [Linux](https://www.linux.org/) and [MacOS](https://www.apple.com/mac/) systems.
+Currently, the SparseZoo Python APIs and CLIs are tested on Python 3.7-3.10 and are limited to [Linux](https://www.linux.org/) and [MacOS](https://www.apple.com/mac/) systems.
 
 ## General Install