Update docs to 3.6.1

Author: JosepSampe

docs/_sources/index.rst

@@ -73,7 +73,7 @@ To start using Lithops:
 
       pip install lithops
 
-2. Configure your cloud credentials (see the full guide in :doc:`/config`)
+2. Configure your cloud credentials (see the full guide in :doc:`Configuration Guide <configuration>`)
 
 3. Write and run your first parallel job:
 

docs/_sources/source/comparing_lithops.rst

@@ -21,11 +21,8 @@ Ray and Dask
 ------------
 
 .. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_logo.png
-   :align: center
    :width: 250
-
 .. image:: https://docs.dask.org/en/stable/_images/dask_horizontal.svg
-   :align: center
    :width: 250
 
 

docs/index.html

@@ -479,7 +479,7 @@ <h2>Get Started Quickly<a class="headerlink" href="#get-started-quickly" title="
 </pre></div>
 </div>
 </li>
-<li><p>Configure your cloud credentials (see the full guide in <span class="xref std std-doc">/config</span>)</p></li>
+<li><p>Configure your cloud credentials (see the full guide in <span class="xref std std-doc">Configuration Guide</span>)</p></li>
 <li><p>Write and run your first parallel job:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">lithops</span>
 

docs/searchindex.js

@@ -425,8 +425,8 @@ <h2>PyWren<a class="headerlink" href="#pywren" title="Permalink to this heading"
 </section>
 <section id="ray-and-dask">
 <h2>Ray and Dask<a class="headerlink" href="#ray-and-dask" title="Permalink to this heading">#</a></h2>
-<a class="reference internal image-reference" href="https://github.com/ray-project/ray/raw/master/doc/source/images/ray_logo.png"><img alt="https://github.com/ray-project/ray/raw/master/doc/source/images/ray_logo.png" class="align-center" src="https://github.com/ray-project/ray/raw/master/doc/source/images/ray_logo.png" style="width: 250px;" /></a>
-<a class="reference internal image-reference" href="https://docs.dask.org/en/stable/_images/dask_horizontal.svg"><img alt="https://docs.dask.org/en/stable/_images/dask_horizontal.svg" class="align-center" src="https://docs.dask.org/en/stable/_images/dask_horizontal.svg" width="250" /></a>
+<a class="reference internal image-reference" href="https://github.com/ray-project/ray/raw/master/doc/source/images/ray_logo.png"><img alt="https://github.com/ray-project/ray/raw/master/doc/source/images/ray_logo.png" src="https://github.com/ray-project/ray/raw/master/doc/source/images/ray_logo.png" style="width: 250px;" /></a>
+<a class="reference internal image-reference" href="https://docs.dask.org/en/stable/_images/dask_horizontal.svg"><img alt="https://docs.dask.org/en/stable/_images/dask_horizontal.svg" src="https://docs.dask.org/en/stable/_images/dask_horizontal.svg" width="250" /></a>
 <p><a class="reference external" href="https://ray.io/">Ray</a> and <a class="reference external" href="https://dask.org/">Dask</a> are distributed computing frameworks designed to operate on a <strong>predefined cluster of nodes</strong> (typically virtual machines). In contrast, Lithops relies on <strong>serverless runtimes</strong>, which allows for <em>elastic and fine-grained scaling</em> — including scaling to zero — with no idle infrastructure costs.</p>
 <p>While Ray and Dask provide dynamic task scheduling and can autoscale within an IaaS environment, they always require a <strong>centralized “head node” or controller</strong> to manage the cluster, making them less suitable for ephemeral and cost-efficient cloud-native computing.</p>
 <p>Additionally, the performance and elasticity of Ray and Dask in IaaS environments are not directly comparable to Lithops’ <strong>fully serverless model</strong>, which benefits from the near-infinite parallelism offered by cloud functions.</p>