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<section id="sanity-check" class="level2">
<h2 class="anchored" data-anchor-id="sanity-check">Sanity check</h2>
<p>To run a sanity check on whether your ray cluster is setup properly, execute the following on the head node:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="ex">ray</span> status</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>The output should have a summary of your Ray cluster - list of all the nodes in your cluster, the number of CPUs and GPUs in your cluster, etc. For example, if you have a cluster with 1 CPU-only head node and 2 4xL40S worker nodes, the output can look like this:</p>
<pre><code>Node status
---------------------------------------------------------------
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<h2 class="anchored" data-anchor-id="configuring-training-with-ray-train">Configuring training with Ray Train</h2>
<p>You can find an example configuration at <code>configs/llama-3/lora-1b-ray.yaml</code>.</p>
<p>The key parameters to note here are:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">use_ray</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb3"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">use_ray</span><span class="kw">:</span><span class="at"> </span><span class="ch">true</span></span>
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<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co"># optional</span></span>
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<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="at"> </span><span class="fu">GPU</span><span class="kw">:</span><span class="at"> </span><span class="dv">1</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<ul>
<li><code>use_ray</code>: This is the flag that enables the Ray Train integration. You can either use the corresponding <code>--use-ray</code> flag in the CLI or set <code>use_ray</code> in the config file.</li>
<li><code>ray_num_workers</code>: This is the number of workers/GPUs to use for training.</li>
<li><code>resources_per_worker</code>: This is the Ray <a href="https://docs.ray.io/en/latest/ray-core/scheduling/resources.html">resource request</a> for each worker. This can be used to request a specific GPU type or a custom resource for each worker. For example, if your ray cluster has GPUs of different types, and you only want to use NVIDIA L40S GPUs, you can do</li>
</ul>
<div class="sourceCode" id="cb4"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">resources_per_worker</span><span class="kw">:</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="at"> accelerator_type</span><span class="fu">:L40S</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.001</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb4"><pre class="sourceCode yaml code-with-copy"><code class="sourceCode yaml"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">resources_per_worker</span><span class="kw">:</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="at"> accelerator_type</span><span class="fu">:L40S</span><span class="kw">:</span><span class="at"> </span><span class="fl">0.001</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</section>
<section id="launching-training" class="level2">
<h2 class="anchored" data-anchor-id="launching-training">Launching training</h2>
<p>You can simply run the following command on the head node:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="ex">axolotl</span> train examples/llama-3/lora-1b-ray.yml <span class="at">--use-ray</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb5"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="ex">axolotl</span> train examples/llama-3/lora-1b-ray.yml <span class="at">--use-ray</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<p>This will launch training on the head node and workers will be scheduled automatically by Ray Train to run on the appropriate head or worker nodes.</p>
<p>You can also monitor training progress on the Ray dashboard.</p>
<p>Coming back to the example on a Ray cluster with 1 head node and 2 4xL40S worker nodes, let’s say you want to make use of all 8 GPUs. You would be able to just set <code>ray_num_workers: 8</code> and run the previous command. The Cluster tab will show the following:</p>
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