[WIP] [Feature] Modular stacks

.pre-commit-config.yaml

@@ -7,7 +7,7 @@ repos:
       - id: check-toml
       - id: check-yaml
         args: ["--unsafe"] # only check syntax for yaml files
-        exclude: ^src/matcha_ml/infrastructure/llm/zen_server
+        exclude: ^(src/matcha_ml/infrastructure/)
       - id: check-json
       - id: mixed-line-ending
         files: "\\.(py|txt|yaml|json|md|toml|lock|cfg|html|sh|js|yml)$"

RELEASE_NOTES.md

@@ -1,3 +1,21 @@
+# v0.2.9
+
+This is a minor release to address a bug and improve documentation based on the changes introduced in v0.2.8.
+
+Date: 23rd August 2023
+
+## Bug Fixes
+
+* Fixed a circular import bug ([#201](https://github.com/fuzzylabs/matcha/pull/201))
+
+## Documentation
+
+* Adds API-based examples to the getting started guide ([#119](https://github.com/fuzzylabs/matcha/pull/199))
+
+See all changes here: https://github.com/fuzzylabs/matcha/compare/v0.2.8...v0.2.9
+
+---
+
 # Stacks 📚
 
 LLMs are all the rage at the moment, with new and improved models being released almost daily. These models are quite large (as implied by the name) and cannot be hosted on standard personal computers, therefore we need to use cloud infrastructure to manage and deploy these models. However, standing up and managing these cloud resources isn't typically the forte of a lot of those interested in LLMs.

docs/getting-started.md

@@ -2,6 +2,8 @@
 
 In this guide, we'll walk you through how to provision your first machine learning infrastructure to Azure, and then use that infrastructure to train and deploy a model. The model we're using is a movie recommender, and we picked this because it's one that beginners can get up and running with quickly.
 
+There are two ways to interact with Matcha; via the CLI tool, or through the API. Throughout this guide we'll demonstrate how to get started using either method.
+
 There are five things we'll cover:
 
 * [Pre-requisites](#pre-requisites): everything you need to set up before starting.
@@ -78,16 +80,27 @@ When you run this command, you'll be taken to the Azure login screen in a web br
 
 Next, let's provision:
 
+CLI:
 ```bash
 matcha provision
 ```
 
-Initially, Matcha will ask you a few questions about how you'd like your infrastructure to be set up. Specifically, it will ask for a _name_ for your infrastructure, a _region_ to deploy it to. Once these details are provided, Matcha will proceed to initialize a remote state manager and ask for a password. After that, it will go ahead of provision infrastructure.
+> Note: users have the choice of passing optional arguments representing the location, prefix, and password parameters by using '--location', '--prefix', or '--password'. For example; `--location uksouth --prefix test123 --password strong_password`. 
+
+API:
+```python
+import matcha_ml.core as matcha
+
+matcha_state_object: MatchaState = matcha.provision(location="uksouth", prefix="test123", password="strong_password")
+```
+
+Initially, Matcha will ask you a few questions about how you'd like your infrastructure to be set up. Specifically, it will ask for a _location_ for your infrastructure, a _prefix_ to deploy it to. Once these details are provided, Matcha will proceed to initialize a remote state manager and ask for a password. After that, it will go ahead of provision infrastructure.
 
 > Note: provisioning can take up to 20 minutes.
 
 Once provisioning is completed, you can query Matcha, using the `get` command:
 
+CLI:
 ```bash
 matcha get
 ```
@@ -152,6 +165,23 @@ Experiment tracker
 
 By default, Matcha will hide sensitive resource properties. If you need one of these properties, then you can add the `--show-sensitive` flag to your `get` command.
 
+API:
+```python
+import matcha_ml.core as matcha
+
+matcha_state_object: MatchaState = matcha.get() 
+```
+
+As with the CLI tool, users have the ability to 'get' specific resources by passing optional `resource_name` and `property_name` arguments to the get function, as demonstrated below:
+
+```python
+import matcha_ml.core as matcha
+
+matcha_state_object: MatchaState = matcha.get(resource_name="experiment_tracker", property_name="flavor")
+```
+
+> Note: the `get()` method will return a `MatchaState` object which represents the provisioned state. The `MatchaState` object contains the `get_component()` method, which will return (where applicable) a `MatchaStateComponent` object representing the specified Matcha state component. In turn, each `MatchaStateComponent` object has a `find_property()` method that will allow the user to be able to access individual component properties.
+
 # 🤝 Sharing resources
 
 You'll notice that a configuration file is create as part of the provisioning process - it's called `matcha.config.json`. This file stores the information necessary for Matcha to identify the resource group and storage container that holds the details of the provisioned resources.
@@ -236,10 +266,18 @@ This will result in a score, which represents how strongly we recommend movie ID
 
 The final thing you'll want to do is decommission the infrastructure that Matcha has set up during this guide. Matcha includes a `destroy` command which will remove everything that has been provisioned, which avoids running up an Azure bill!
 
+CLI:
 ```bash
 matcha destroy
 ```
 
+API:
+```python
+import matcha_ml.core as matcha
+
+matcha.destroy()
+```
+
 > Note: that this command is irreversible will remove all the resources deployed by `matcha provision` including the resource group, so make sure you save any data you wish to keep before running this command.
 >
 > You may also notice that an additional resource has appeared in Azure called 'NetworkWatcherRG' (if it wasn't already there). This is a resource that is automatically provisioned by Azure in each region when there is in-coming traffic to a provisioned resource and isn't controlled by Matcha. More information can be found [here](https://learn.microsoft.com/en-us/azure/network-watcher/network-watcher-monitoring-overview) on how to manage or remove this resource.