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FastEmbed
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FastEmbed Multi GPU
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⚡️ What is FastEmbed?
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<a href="../../Getting%20Started/" class="md-nav__link">
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Getting Started
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Examples
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Examples
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ColBERT with FastEmbed
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FastEmbed GPU
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FastEmbed Multi GPU
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FastEmbed Multi GPU
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Fastembed Multi-GPU Tutorial
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Prerequisites
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<a href="#multi-gpu-using-cuda-argument-with-textembedding-model" class="md-nav__link">
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Multi-GPU using cuda argument with TextEmbedding Model
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FastEmbed vs HF Comparison
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<a href="../Hindi_Tamil_RAG_with_Navarasa7B/" class="md-nav__link">
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Hindi Tamil RAG with Navarasa7B
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Hybrid Search
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<h1>FastEmbed Multi GPU</h1>
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<h3 id="fastembed-multi-gpu-tutorial">Fastembed Multi-GPU Tutorial</h3>
<p>This tutorial demonstrates how to leverage multi-GPU support in Fastembed. Fastembed supports embedding text and images utilizing modern GPUs for acceleration. Let's explore how to use Fastembed with multiple GPUs step by step.</p>
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<h4 id="prerequisites">Prerequisites</h4>
<p>To get started, ensure you have the following installed:
- Python 3.9 or later
- Fastembed (<code>pip install fastembed-gpu</code>)
- Refer to <a href="https://github.com/qdrant/fastembed/blob/main/docs/examples/FastEmbed_GPU.ipynb">this</a> tutorial if you have issues with GPU dependencies
- Access to a multi-GPU server</p>
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<h3 id="multi-gpu-using-cuda-argument-with-textembedding-model">Multi-GPU using cuda argument with TextEmbedding Model</h3>
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<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">fastembed</span><span class="w"> </span><span class="kn">import</span> <span class="n">TextEmbedding</span>
<span class="c1"># define the documents to embed</span>
<span class="n">docs</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;hello world&quot;</span><span class="p">,</span> <span class="s2">&quot;flag embedding&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span>
<span class="c1"># define gpu ids</span>
<span class="n">device_ids</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
<span class="c1"># initialize a TextEmbedding model using CUDA</span>
<span class="n">text_model</span> <span class="o">=</span> <span class="n">TextEmbedding</span><span class="p">(</span>
<span class="n">model_name</span><span class="o">=</span><span class="s2">&quot;sentence-transformers/all-MiniLM-L6-v2&quot;</span><span class="p">,</span>
<span class="n">cuda</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">device_ids</span><span class="o">=</span><span class="n">device_ids</span><span class="p">,</span>
<span class="n">lazy_load</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># generate embeddings</span>
<span class="n">text_embeddings</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">text_model</span><span class="o">.</span><span class="n">embed</span><span class="p">(</span><span class="n">docs</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">parallel</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">device_ids</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">text_embeddings</span><span class="p">)</span>
</code></pre></div>
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<p>In this snippet:
- <code>cuda=True</code> enables GPU acceleration.
- <code>device_ids=[0, 1]</code> specifies GPUs to use. Replace <code>[0, 1]</code> with available GPU IDs.
- <code>lazy_load=True</code></p>
<p><strong>NOTE</strong>: When using multi-GPU settings, it is important to configure <code>parallel</code> and <code>lazy_load</code> properly to avoid inefficiencies:</p>
<p><code>parallel</code>: This parameter enables multi-GPU support by spawning child processes for each GPU specified in device_ids. To ensure proper utilization, the value of <code>parallel</code> must match the number of GPUs in device_ids. If using a single GPU, this parameter is not necessary.</p>
<p><code>lazy_load</code>: Enabling <code>lazy_load</code> prevents redundant memory usage. Without <code>lazy_load</code>, the model is initially loaded into the memory of the first GPU by the main process. When child processes are spawned for each GPU, the model is reloaded on the first GPU, causing redundant memory consumption and inefficiencies.</p>
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