Update docs to 3.6.1

Author: JosepSampe

docs/_sources/source/api_futures.rst

@@ -7,7 +7,8 @@ The core abstraction in Lithops is the **executor**, responsible for orchestrati
 
 To get started, you typically import `lithops` and create an executor instance to run your code. Lithops provides a flexible set of executors to suit different needs.
 
-### Primary Executors
+Primary Executors
+-----------------
 
 * **FunctionExecutor** (`lithops.FunctionExecutor()`):  
   The main, generic executor that automatically selects its execution mode based on the provided configuration.  
@@ -17,7 +18,8 @@ To get started, you typically import `lithops` and create an executor instance t
   A robust wrapper around `FunctionExecutor` that transparently handles retries on failed tasks.  
   It supports all features of `FunctionExecutor` with added automatic retry logic, improving fault tolerance and reliability for unstable or transient failure-prone environments.
 
-### Secondary Executors
+Secondary Executors
+-------------------
 
 For more specialized use cases, Lithops also provides explicit executors for each execution mode:
 
@@ -30,14 +32,12 @@ For more specialized use cases, Lithops also provides explicit executors for eac
 * **StandaloneExecutor** (`lithops.StandaloneExecutor()`):  
   Runs jobs on standalone compute backends such as clusters or virtual machines, suitable for long-running or resource-heavy tasks.
 
----
 
-### Configuration and Initialization
+Configuration and Initialization
+================================
 
 By default, executors load configuration from the Lithops configuration file (e.g., `lithops_config.yaml`). You can also supply configuration parameters programmatically via a Python dictionary when creating an executor instance. Parameters passed explicitly override those in the config file, allowing for flexible customization on the fly.
 
----
-
 This layered executor design lets Lithops provide a powerful, unified API for parallel function execution — from local development to multi-cloud production deployments with fault tolerance and retries built-in.
 
 

docs/_sources/source/compute_config/kubernetes_rabbitmq.md

@@ -4,7 +4,7 @@
 
 All of these changes are **ideal** for pipelines where launching **hundreds of parallel tasks as quickly as possible** is a critical requirement, in a fixed size heterogeneous cluster.
 
-### Changes of K8s RabbitMQ
+## Changes of K8s RabbitMQ
 
 * **Utilization of RabbitMQ:** Within this architecture, RabbitMQ is employed to launch group invocations in a single call, avoiding the need for multiple calls for each function execution. Additionally, it enables data exchange between the client and running pods, bypassing the Storage Backend as an intermediary, which is slower. This accelerates and streamlines communication significantly.
 
@@ -74,25 +74,25 @@ All of these tests consist of running 225 functions on a 2-node cluster, each wi
 
 In this scenario, it is evident that the invocation time is consistently reduced by a factor of **up to 5x** on cold start and **up to 7x** on warm start. This represents a significant enhancement for parallel function execution.
 
-#### Plot 1: Kubernetes K8s original. 
+- Plot 1: Kubernetes K8s original. 
 
 *Elapsed time = 16,9 sec.*
 
 ![Kubernetes K8s original plot](../images/plots_kubernetes/k8s_original_histogram.png)
 
-#### Plot 2: Kubernetes K8s original with master on Warm Start. 
+- Plot 2: Kubernetes K8s original with master on Warm Start. 
 
 *Elapsed time = 8,1 sec.*
 
 ![Kubernetes K8s original with Warm Start plot](../images/plots_kubernetes/k8s_original_warm_start_histogram.png)
 
-#### Plot 3: Kubernetes K8s RabbitMQ. 
+- Plot 3: Kubernetes K8s RabbitMQ. 
 
 *Elapsed time = 8 sec.*
 
 ![Kubernetes K8s RabbitMQ plot](../images/plots_kubernetes/rabbitmq_histogram.png)
 
-#### Plot 4: Kubernetes K8s RabbitMQ with workers on Warm Start. 
+- Plot 4: Kubernetes K8s RabbitMQ with workers on Warm Start. 
 
 *Elapsed time = 5,9 sec.*
 

docs/_sources/source/contributing.rst

@@ -18,24 +18,39 @@ To contribute a patch
 1. Break your work into small, single-purpose patches if possible. It's much
    harder to merge in a large change with a lot of disjoint features.
 2. Submit the patch as a GitHub pull request against the master branch.
-3. Make sure that your code passes the unit tests.
-4. Make sure that your code passes the linter.
-5. Add new unit tests for your code.
-
-
-Unit testing
-------------
-
-To test that all is working as expected, run either:
-
-.. code::
-
-   $ lithops test
-
-
-.. code::
-
-   $ python3 -m lithops.tests.tests_main
-
-
-Please follow the guidelines in :ref:`testing` for more details.
+3. Make sure that your code passes the tests.
+4. Make sure that your code passes the linter. Install `flake8` with `pip3 install flake8` and run the next command until you don't see any linitng error:
+   ```bash
+   flake8 lithops --count --max-line-length=180 --statistics --ignore W605,W503
+   ``` 
+5. Add new tests for your code.
+
+
+Testing
+-------
+
+To test that all is working as expected, you must install `pytest`, navigate to the tests folder `lithops/tests/`, and execute:
+```bash
+pytest -v
+```
+
+If you made changes to a specific backend, please run tests on that backend.
+For example, if you made changes to the AWS Lambda backend, execute the tests with:
+```bash
+pytest -v --backend aws_lambda --storage aws_s3
+```
+
+You can list all the available tests using:
+```bash
+pytest --collect-only
+```
+
+To run a specific test or group of tests, use the `-k` parameter, for example:
+```bash
+pytest -v --backend localhost --storage localhost -k test_map
+```
+
+To view all the Lithops logs during the tests, and in DEBUG mode, execute:
+```bash
+pytest -o log_cli=true --log-cli-level=DEBUG --backend localhost --storage localhost
+```

docs/genindex.html

@@ -206,6 +206,7 @@
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Lithops Compute API</span></p>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="source/api_futures.html">Lithops Futures API</a></li>
+
 <li class="toctree-l1"><a class="reference internal" href="source/functions.html">Functions and Parameters</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/worker_granularity.html">Worker Granularity</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/notebooks/function_chaining.html">Function chaining</a></li>

docs/index.html

@@ -208,6 +208,7 @@
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Lithops Compute API</span></p>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="source/api_futures.html">Lithops Futures API</a></li>
+
 <li class="toctree-l1"><a class="reference internal" href="source/functions.html">Functions and Parameters</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/worker_granularity.html">Worker Granularity</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/notebooks/function_chaining.html">Function chaining</a></li>

docs/objects.inv

@@ -209,6 +209,7 @@
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Lithops Compute API</span></p>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="source/api_futures.html">Lithops Futures API</a></li>
+
 <li class="toctree-l1"><a class="reference internal" href="source/functions.html">Functions and Parameters</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/worker_granularity.html">Worker Granularity</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/notebooks/function_chaining.html">Function chaining</a></li>

docs/py-modindex.html

@@ -208,6 +208,7 @@
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Lithops Compute API</span></p>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="source/api_futures.html">Lithops Futures API</a></li>
+
 <li class="toctree-l1"><a class="reference internal" href="source/functions.html">Functions and Parameters</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/worker_granularity.html">Worker Granularity</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/notebooks/function_chaining.html">Function chaining</a></li>

docs/search.html

@@ -209,6 +209,7 @@
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Lithops Compute API</span></p>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="api_futures.html">Lithops Futures API</a></li>
+
 <li class="toctree-l1"><a class="reference internal" href="functions.html">Functions and Parameters</a></li>
 <li class="toctree-l1"><a class="reference internal" href="worker_granularity.html">Worker Granularity</a></li>
 <li class="toctree-l1"><a class="reference internal" href="notebooks/function_chaining.html">Function chaining</a></li>