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assets/data/benchmarks/memory-recomputation.html

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dist/index.html

@@ -297,10 +297,10 @@
 
         <p>Using this snippet [TODO: link to appendix A5], we can understand how memory is allocated throughout training. We can see that memory utilization is not a static thing but varies a lot during training and during a training step:</p>
 
-        <div class="svg-container l-body-outset" id="svg-first_steps_memory_profile"> </div>
-        <script src="../assets/images/first_steps_memory_profile.js"></script>
+        <!--<div class="svg-container l-body-outset" id="svg-first_steps_memory_profile"> </div>
+        <script src="../assets/images/first_steps_memory_profile.js"></script>-->
 
-        <!-- <p><img alt="llama-1b-memory.png" src="assets/images/placeholder.png" /></p> -->
+        <iframe id="plotFrame" src="assets/data/benchmarks/memory-profile.html" height="520" width="1000" scrolling="no" frameborder="0"></iframe>
 
         <p>Clearly the first step looks very different from the subsequent ones, but let’s first have a look at the general anatomy of a step: first the activations increase quickly as we do the forward pass, then during the backward pass the gradients build up and as the backward pass propagates, the stored activations used to compute the gradients are progressively cleared. Finally, we perform the optimization step during which we need all the gradients and then update the optimizer states before we start the next forward pass. </p>
 
@@ -442,7 +442,9 @@
 
         <p>Let’s see how drastically recomputation strategies can in practice reduce the memory footprint and how selective recomputation strikes a nice balance between memory saving and recomputation cost:</p>
 
-        <p><img alt="llama-8b-memory-bars--recomp.png" src="/assets/images/placeholder.png" /></p>
+        <iframe id="plotFrame" src="assets/data/benchmarks/memory-recomputation.html" height="540" width="1000" scrolling="no" frameborder="0"></iframe>
+
+        Another trend that's clearly visibile here is how the activations for long sequences play a bigger role for smaller models, so the effect of recomputation becomes even more noticeable. 
 
         <div class="note-box">
             <p class="note-box-title">📝 Note</p>

src/index.html

@@ -297,10 +297,10 @@
 
         <p>Using this snippet [TODO: link to appendix A5], we can understand how memory is allocated throughout training. We can see that memory utilization is not a static thing but varies a lot during training and during a training step:</p>
 
-        <div class="svg-container l-body-outset" id="svg-first_steps_memory_profile"> </div>
-        <script src="../assets/images/first_steps_memory_profile.js"></script>
+        <!--<div class="svg-container l-body-outset" id="svg-first_steps_memory_profile"> </div>
+        <script src="../assets/images/first_steps_memory_profile.js"></script>-->
 
-        <!-- <p><img alt="llama-1b-memory.png" src="assets/images/placeholder.png" /></p> -->
+        <iframe id="plotFrame" src="assets/data/benchmarks/memory-profile.html" height="520" width="1000" scrolling="no" frameborder="0"></iframe>
 
         <p>Clearly the first step looks very different from the subsequent ones, but let’s first have a look at the general anatomy of a step: first the activations increase quickly as we do the forward pass, then during the backward pass the gradients build up and as the backward pass propagates, the stored activations used to compute the gradients are progressively cleared. Finally, we perform the optimization step during which we need all the gradients and then update the optimizer states before we start the next forward pass. </p>
 
@@ -442,7 +442,9 @@
 
         <p>Let’s see how drastically recomputation strategies can in practice reduce the memory footprint and how selective recomputation strikes a nice balance between memory saving and recomputation cost:</p>
 
-        <p><img alt="llama-8b-memory-bars--recomp.png" src="/assets/images/placeholder.png" /></p>
+        <iframe id="plotFrame" src="assets/data/benchmarks/memory-recomputation.html" height="540" width="1000" scrolling="no" frameborder="0"></iframe>
+
+        Another trend that's clearly visibile here is how the activations for long sequences play a bigger role for smaller models, so the effect of recomputation becomes even more noticeable. 
 
         <div class="note-box">
             <p class="note-box-title">📝 Note</p>