Updated REST API description

2025R2/AVX_Autonomy_REST_API_25R2/AVX_Explore_API_V1_ArchV2/changelog.md

@@ -1,3 +1,22 @@
 # Changelog
 
-## 2025 R2.1
+This file lists the changes introduced in the AVxcelerate Explore API v0.1.0 released in 2025 R2. 
+
+The AVxcelerate Explore API v0.1.0 is compatible with the AVX Architecture V2.
+
+The AVxcelerate Explore API v0.1.0 allows you to perform operations related to jobs and create post job sessions.
+
+## Features
+
+### Jobs:
+    The API allows you to perform the following operations related to jobs:
+* Create a new job as per provided parameters
+* Get a single or multiple jobs by their IDs
+* Kill a running job
+* Restart a completed or killed job
+* Clone a job
+### Post Job Sessions:
+* This API allows you to create a post job session for a job and returns the relative URL to access the session.
+* The session can then be used to perform various post-processing operations such as: viewing results, analyzing data etc.
+
+

2025R2/AVX_Autonomy_REST_API_25R2/AVX_Explore_API_V1_ArchV2/intro_archv2_.md → 2025R2/AVX_Autonomy_REST_API_25R2/AVX_Explore_API_V1_ArchV2/intro_archv2_explore.md

@@ -1,12 +1,26 @@
-# AVx Python APIs Documentation
 
-## Background
 
-The AVx python APIs are hosted as a python package on cluster as part of the Explore service deployment. The developers can install the package using pip and use it to call AVx autonomy APIs without the need to make raw REST calls.
+## Features 
+
+### Jobs:
+    The API allows you to perform the following operations related to jobs:
+* Create a new job as per provided parameters
+* Get a single or multiple jobs by their IDs
+* Kill a running job
+* Restart a completed or killed job
+* Clone a job
+### Post Job Sessions:
+* This API allows you to create a post job session for a job and returns the relative URL to access the session.
+* The session can then be used to perform various post-processing operations such as: viewing results, analyzing data etc.
+
+
+## Introduction
+
+The AVx python APIs are hosted as a python package on a cluster as part of the Explore service deployment. The developers can install the package using pip and use it to call AVx autonomy APIs without the need to make raw REST calls.
 
 PyPi Regsitry URL:
 
-The python package is hosted as PyPi compliant registry on each deployed cluster. The registry URL looks like this:
+The python package is hosted as PyPi compliant registry on each deployed cluster. The registry URL is as written below:
 
 https://BASE_URL/pypi
 
@@ -18,7 +32,8 @@ For example, for AFT deployment:
 
 Pre-requisites:
 
-We are assuming that on the system is running **Ubuntu 22.04**, there are following already installed:
+
+We assume that on the system is running on the **Ubuntu 22.04** version, and that the following tools are already installed:
 
 - python 3.10
 - pip 25.1

2025R2/AVX_Autonomy_REST_API_25R2/AVX_Explore_Resources_API_V1_ArchV2/changelog.md

@@ -1,3 +1,24 @@
 # Changelog
 
-## 2025 R2.1
+This file lists the changes introduced in the AVxcelerate Resource Manager API v0.1.0 released in 2025 R2.
+
+The AVxcelerate Resource Manager API v0.1.0 is compatible with the AVX Architecture V2.
+
+This API allows to perform CRUD (Create, Read, Update, and Delete) operations on resources such as queues, deployments, applications and app-runtime-configurations.
+
+## Features
+
+### Queues:
+* You can create queues with the required storages, resource limits and environment variables
+* Allowing to manage queues helps will help you configure different applications within resource limits and group the applications requiring same storage together.
+* You can adjust the maximum number of workers that can concurrently run on a queue using the parameter 'maximum_allowed_worker_instances'
+
+### Plugins:
+ You can register a plugin with definition of container runtime.
+For example: Docker Engine / Kubernetes
+
+### Jobs:
+* You can submit a resource-manager job by providing application details (name, version, image, environment variables, etc.) and track it to its completion.
+* You can also check the status of the job and clean the resources the job has acquired.
+
+

2025R2/AVX_Autonomy_REST_API_25R2/AVX_Explore_Resources_API_V1_ArchV2/intro_archv2_.md → 2025R2/AVX_Autonomy_REST_API_25R2/AVX_Explore_Resources_API_V1_ArchV2/intro_archv2_resources_api.md

@@ -1,12 +1,28 @@
-# AVx Python APIs Documentation
 
-## Background
+## Features 
 
-The AVx python APIs are hosted as a python package on cluster as part of the Explore service deployment. The developers can install the package using pip and use it to call AVx autonomy APIs without the need to make raw REST calls.
+This API allows to perform CRUD (Create, Read, Update, and Delete) operations on resources such as queues, deployments, applications and app-runtime-configurations.
+
+### Queues:
+* You can create queues with the required storages, resource limits and environment variables
+* Allowing to manage queues helps will help you configure different applications within resource limits and group the applications requiring same storage together.
+* You can adjust the maximum number of workers that can concurrently run on a queue using the parameter 'maximum_allowed_worker_instances'
+
+### Plugins:
+* You can register a plugin with definition of container runtime.
+For example: Docker Engine / Kubernetes
+
+### Jobs:
+* You can submit a resource-manager job by providing application details (name, version, image, environment variables, etc.) and track it to its completion.
+You can also check the status of the job and clean the resources the job has acquired.
+
+## Introduction
+
+The AVxcelerate python APIs are hosted as a python package on a cluster as part of the Explore service deployment. The developers can install the package using pip and use it to call AVx autonomy APIs without needing to make raw REST calls.
 
 PyPi Regsitry URL:
 
-The python package is hosted as PyPi compliant registry on each deployed cluster. The registry URL looks like this:
+The python package is hosted as PyPi compliant registry on each deployed cluster. The registry URL is like this:
 
 https://BASE_URL/pypi
 
@@ -18,7 +34,7 @@ For example, for AFT deployment:
 
 Pre-requisites:
 
-We are assuming that on the system is running **Ubuntu 22.04**, there are following already installed:
+We assume that on the system is running with the **Ubuntu 22.04** version, and that the following tools are already installed:
 
 - python 3.10
 - pip 25.1
@@ -28,13 +44,13 @@ And we assume that you are using AVx Autonomy Toolchain version **25R2.1**
 
 Step 1: Create virtual environment
 
-```
+```bash
 $ python -m venv .venv 
 ``` 
 
 Step 2: Activate the virtual environment
 
-```
+```bash
 $ source .venv/bin/activate  
 ```
 
@@ -43,13 +59,14 @@ Step 3: Install python packages:
 - ansys-api-avxcelerate-autonomy
 - ansys-avxcelerate-autonomy
 
-```
+```bash
 $ pip install ansys-api-avxcelerate-autonomy ansys-avxcelerate-autonomy --extra-index-url [https://explore-service.traefik.me:9081/pypi](https://explore-service.traefik.me:9081/v1/pypi)
+```
 
 Step 4: Use ansys-api-avxcelerate-autonomy and ansys-avxcelerate-autonomy in your python code
-```
 
-```
+
+```python
 import asyncio
 
 from ansys.api.avxcelerate.autonomy.explore_service.v1.api.jobs_api import JobsApi
@@ -105,4 +122,5 @@ print(str(ex))
 print("Couldn't get job against this job id")
 
 asyncio.run(main())
-```
+```
+

2025R2/dpf-framework-25-r2/core-concepts/dpf-types.md

@@ -63,10 +63,10 @@ The ``data_sources`` holds information on files of interest such as:
 - their associated namespace
 - their associated key (usually the file extension)
 
-#### Streaming
-
 <a id="streams-container"></a>
 
+#### Streaming
+
 The ``streams_container`` is the result of opening a stream to files in a ``data_sources``.
 It enables streaming to and from the files and handles caching of data requests to the files.
 
@@ -76,58 +76,59 @@ The use of a ``streams_container`` is highly recommended whenever possible to be
 
 The following DPF objects hold light-weight data (metadata) relative to other DPF types.
 
-#### Field metadata
-
 <a id="field-definition"></a>
 
-The ``field_definition`` holds metadata relative to a ``field``.
+#### Field metadata
 
-#### Mesh metadata
+The ``field_definition`` holds metadata relative to a ``field``.
 
 <a id="mesh-info"></a>
 
+#### Mesh metadata
+
 The ``mesh_info`` holds metadata relative to a ``meshed_region``.
 
 Only available for CFF and LSDYNA.
 
-#### Result file metadata
-
 <a id="result-info"></a>
 
-The ``result_info`` holds metadata relative to available results in files from a ``data_sources``.
+#### Result file metadata
 
-### Data supports
+The ``result_info`` holds metadata relative to available results in files from a ``data_sources``.
 
 <a id="support"></a>
 
+### Data supports
+
 Supports are entities dedicated to holding information about the model itself.
 
 Every ``field`` requires a ``support`` for DPF to understand what its data is related to.
 
 This concept allows DPF to manage simulation data efficiently.
 
-#### Mesh
 
 <a id="meshed-region"></a>
 
+#### Mesh
+
 The ``meshed_region`` holds information relative to the mesh of the simulation model.
 
 It gives access to several fields of data such as:
 - the mesh connectivity
 - the node coordinates
 - the element types
 
-#### Time and frequency
-
 <a id="time-freq-support"></a>
 
+#### Time and frequency
+
 For time and pseudo time based simulations or for frequency based simulations, the ``time_freq_support`` holds information about the 
 simulation steps and sub-steps, time-steps, or mode/harmonic frequencies.
 
-#### Cyclic
-
 <a id="cyclic-support"></a>
 
+#### Cyclic
+
 For cyclic simulation models, the ``cyclic_support`` holds information about the number of sectors and the number of stages.
 
 ### Filtering
@@ -156,10 +157,10 @@ Each ``field`` data storage type has a ``scoping`` associated to it, describing
 
 The following DPF types allow you to store and describe data.
 
-#### Data map
-
 <a id="generic-data-container"></a>
 
+#### Data map
+
 The ``generic_data_container`` allows you to store any type known to DPF as a property with a given name.
 
 #### Data tree
@@ -168,10 +169,10 @@ The ``generic_data_container`` allows you to store any type known to DPF as a pr
 
 The ``data_tree`` allows you to store DPF known types as named attributes of a data tree with sub-trees.
 
-#### Data arrays
-
 <a id="arrays"></a>
 
+#### Data arrays
+
 The following types represent arrays of data.
 
 The data of a ``field`` is always associated to a ``scoping`` (entities associated to each value) and ``support`` (subset of the model where the data is), making the ``field`` a self-describing piece of data.
@@ -180,28 +181,28 @@ Example 1: a ``field`` that describes the evolution in time of the static pressu
 
 Example 2: a ``field`` that describes the evolution in space of the stress at a given body of a structural model has a scoping comprised of the element ids where the stress is defined and a ``meshed_region`` support that contextualizes these element ids. 
 
-##### Doubles
-
 <a id="field"></a>
 
-The ``field`` represents an array of double values.
+##### Doubles
 
-##### Integers
+The ``field`` represents an array of double values.
 
 <a id="property-field"></a>
 
-The ``property_field`` represents an array of integer values.
+##### Integers
 
-##### Strings
+The ``property_field`` represents an array of integer values.
 
 <a id="string-field"></a>
 
-The ``string_field`` represents an array of string values.
+##### Strings
 
-##### Custom
+The ``string_field`` represents an array of string values.
 
 <a id="custom-type-field"></a>
 
+##### Custom
+
 The ``custom_type_field`` represents an array of values of a custom type as defined by the unitary type of the field.
 
 ### Collections
@@ -213,6 +214,7 @@ DPF allows you to group DPF types in labeled collections.
 A DPF ``collection`` has a set of associated labels, for which each entry has a value. This allows you to distinguish between entries and retrieve them. 
 
 #### Label space
+
 <a id="label-space"></a>
 
 The ``label_space`` is a map of ("label": integer value) couples used to target a subset of entries in a collection.
@@ -221,46 +223,46 @@ For example, if a ``collection`` has labels ``material`` and ``part``, each enti
 
 A ``label_space`` such as ``{"material": X, "part": Y}`` then targets a single entity in the collection, whereas one such as ``{"material": X}`` targets all entries of material "X".
 
-#### Base collection
-
 <a id="collection"></a>
 
-The ``collection`` is the generic type for collections of DPF entities.
+#### Base collection
 
-#### Collection of any
+The ``collection`` is the generic type for collections of DPF entities.
 
 <a id="any-collection"></a>
 
-The ``any_collection`` is a collection of ``Any`` objects. 
+#### Collection of any
 
-#### Collection of fields of custom type
+The ``any_collection`` is a collection of ``Any`` objects. 
 
 <a id="custom-type-fields-container"></a>
 
-The ``custom_type_fields_container`` is a collection of ``custom_type_field`` instances.
+#### Collection of fields of custom type
 
-#### Collection of fields of doubles
+The ``custom_type_fields_container`` is a collection of ``custom_type_field`` instances.
 
 <a id="fields-container"></a>
 
-The ``fields_container`` is a collection of ``field`` instances.
+#### Collection of fields of doubles
 
-#### Collection of fields of integers
+The ``fields_container`` is a collection of ``field`` instances.
 
 <a id="property-fields-container"></a>
 
-The ``property_fields_container`` is a collection of ``property_field`` instances.
+#### Collection of fields of integers
 
-#### Collection of meshes
+The ``property_fields_container`` is a collection of ``property_field`` instances.
 
 <a id="meshes-container"></a>
 
-The ``meshes_container`` is a collection of ``meshed_region`` instances.
+#### Collection of meshes
 
-#### Collection of scopings
+The ``meshes_container`` is a collection of ``meshed_region`` instances.
 
 <a id="scopings-container"></a>
 
+#### Collection of scopings
+
 The ``scopings_container`` is a collection of ``scoping`` instances.
 
 ### Unit systems