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@@ -318,6 +318,26 @@
 
 
 
+ <li class="md-nav__item">
+ <a href="/free_energy/" class="md-nav__link">
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+ <span class="md-ellipsis">
+ free energy calculations
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configuration/index.html

@@ -9,7 +9,7 @@
 
 
 
- <link rel="prev" href="../learning/">
+ <link rel="prev" href="../free_energy/">
 
 
 
@@ -323,6 +323,26 @@
 
 
 
+
+
+ <li class="md-nav__item">
+ <a href="../free_energy/" class="md-nav__link">
+
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+ <span class="md-ellipsis">
+ free energy calculations
+ </span>
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+ </li>
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+
 
 
 
@@ -613,19 +633,17 @@ <h3 id="1-ml-potential-training">1. ML potential training</h3>
 SLURM jobscripts, send them to the scheduler, and once the resources are allocated,
 start the calculation. For example, assume that the GPU partition on this cluster is
 named <code>infinite_a100</code>, and it has 12 cores per GPU. Consider the following config
-<div class="highlight"><pre><span></span><code><a id="__codelineno-10-1" name="__codelineno-10-1" href="#__codelineno-10-1"></a><span class="nt">container_runtime</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">apptainer</span><span class="w"> </span><span class="c1"># or singularity; check HPC docs to see which one is available</span>
-<a id="__codelineno-10-2" name="__codelineno-10-2" href="#__codelineno-10-2"></a><span class="nt">container_uri</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">oras://ghcr.io/molmod/psiflow:main_cu118</span><span class="w"> </span><span class="c1"># built from github main branch</span>
-<a id="__codelineno-10-3" name="__codelineno-10-3" href="#__codelineno-10-3"></a><span class="nt">ModelTraining</span><span class="p">:</span>
-<a id="__codelineno-10-4" name="__codelineno-10-4" href="#__codelineno-10-4"></a><span class="w"> </span><span class="nt">cores_per_worker</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">12</span>
-<a id="__codelineno-10-5" name="__codelineno-10-5" href="#__codelineno-10-5"></a><span class="w"> </span><span class="nt">gpu</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
-<a id="__codelineno-10-6" name="__codelineno-10-6" href="#__codelineno-10-6"></a><span class="w"> </span><span class="nt">slurm</span><span class="p">:</span>
-<a id="__codelineno-10-7" name="__codelineno-10-7" href="#__codelineno-10-7"></a><span class="w"> </span><span class="nt">partition</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;infinite_a100&quot;</span>
-<a id="__codelineno-10-8" name="__codelineno-10-8" href="#__codelineno-10-8"></a><span class="w"> </span><span class="nt">account</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;112358&quot;</span>
-<a id="__codelineno-10-9" name="__codelineno-10-9" href="#__codelineno-10-9"></a><span class="w"> </span><span class="nt">nodes_per_block</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">1</span>
-<a id="__codelineno-10-10" name="__codelineno-10-10" href="#__codelineno-10-10"></a><span class="w"> </span><span class="nt">cores_per_node</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">24</span>
-<a id="__codelineno-10-11" name="__codelineno-10-11" href="#__codelineno-10-11"></a><span class="w"> </span><span class="nt">max_blocks</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">1</span>
-<a id="__codelineno-10-12" name="__codelineno-10-12" href="#__codelineno-10-12"></a><span class="w"> </span><span class="nt">walltime</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;12:00:00&quot;</span>
-<a id="__codelineno-10-13" name="__codelineno-10-13" href="#__codelineno-10-13"></a><span class="w"> </span><span class="nt">scheduler_options</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;#SBATCH</span><span class="nv"> </span><span class="s">--gpus=2&quot;</span>
+<div class="highlight"><pre><span></span><code><a id="__codelineno-10-1" name="__codelineno-10-1" href="#__codelineno-10-1"></a><span class="nt">ModelTraining</span><span class="p">:</span>
+<a id="__codelineno-10-2" name="__codelineno-10-2" href="#__codelineno-10-2"></a><span class="w"> </span><span class="nt">cores_per_worker</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">12</span>
+<a id="__codelineno-10-3" name="__codelineno-10-3" href="#__codelineno-10-3"></a><span class="w"> </span><span class="nt">gpu</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
+<a id="__codelineno-10-4" name="__codelineno-10-4" href="#__codelineno-10-4"></a><span class="w"> </span><span class="nt">slurm</span><span class="p">:</span>
+<a id="__codelineno-10-5" name="__codelineno-10-5" href="#__codelineno-10-5"></a><span class="w"> </span><span class="nt">partition</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;infinite_a100&quot;</span>
+<a id="__codelineno-10-6" name="__codelineno-10-6" href="#__codelineno-10-6"></a><span class="w"> </span><span class="nt">account</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;112358&quot;</span>
+<a id="__codelineno-10-7" name="__codelineno-10-7" href="#__codelineno-10-7"></a><span class="w"> </span><span class="nt">nodes_per_block</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">1</span>
+<a id="__codelineno-10-8" name="__codelineno-10-8" href="#__codelineno-10-8"></a><span class="w"> </span><span class="nt">cores_per_node</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">24</span>
+<a id="__codelineno-10-9" name="__codelineno-10-9" href="#__codelineno-10-9"></a><span class="w"> </span><span class="nt">max_blocks</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">1</span>
+<a id="__codelineno-10-10" name="__codelineno-10-10" href="#__codelineno-10-10"></a><span class="w"> </span><span class="nt">walltime</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;12:00:00&quot;</span>
+<a id="__codelineno-10-11" name="__codelineno-10-11" href="#__codelineno-10-11"></a><span class="w"> </span><span class="nt">scheduler_options</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;#SBATCH</span><span class="nv"> </span><span class="s">--gpus=2&quot;</span>
 </code></pre></div>
 The top-level keyword <code>ModelTraining</code> indicates that we're defining the execution of
 <code>model.train()</code>. It has a number of special keywords:</p>
@@ -659,7 +677,7 @@ <h3 id="1-ml-potential-training">1. ML potential training</h3>
  achieve optimal training performance. For example, on some clusters, it is
  necessary to tune the process/thread affinity a little bit. For example:
  <div class="highlight"><pre><span></span><code><a id="__codelineno-11-1" name="__codelineno-11-1" href="#__codelineno-11-1"></a><span class="nt">env_vars</span><span class="p">:</span>
-<a id="__codelineno-11-2" name="__codelineno-11-2" href="#__codelineno-11-2"></a><span class="w"> </span><span class="nt">OMP_PROC_BIND</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">spread</span>
+<a id="__codelineno-11-2" name="__codelineno-11-2" href="#__codelineno-11-2"></a><span class="w"> </span><span class="nt">OMP_PROC_BIND</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;spread&quot;</span>
 </code></pre></div></li>
 </ul>
 <h3 id="2-molecular-dynamics">2. molecular dynamics</h3>

data/index.html

@@ -390,6 +390,26 @@
 
 
 
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