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<h1 class="title">Inference Endpoints</h1>
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<section id="how-to-use-inference-endpoints-to-embed-documents" class="level1">
<h1>How to use Inference Endpoints to Embed Documents</h1>
<p><em>Authored by: <a href="https://huggingface.co./derek-thomas">Derek Thomas</a></em></p>
<section id="goal" class="level2">
<h2 class="anchored" data-anchor-id="goal">Goal</h2>
<p>I have a dataset I want to embed for semantic search (or QA, or RAG), I want the easiest way to do embed this and put it in a new dataset.</p>
</section>
<section id="approach" class="level2">
<h2 class="anchored" data-anchor-id="approach">Approach</h2>
<p>I’m using a dataset from my favorite subreddit <a href="https://www.reddit.com/r/bestofredditorupdates/">r/bestofredditorupdates</a>. Because it has long entries, I will use the new <a href="https://huggingface.co./jinaai/jina-embeddings-v2-base-en">jinaai/jina-embeddings-v2-base-en</a> since it has an 8k context length. I will deploy this using <a href="https://huggingface.co./inference-endpoints">Inference Endpoint</a> to save time and money. To follow this tutorial, you will need to <strong>have already added a payment method</strong>. If you haven’t, you can add one here in <a href="https://huggingface.co./docs/hub/billing#billing">billing</a>. To make it even easier, I’ll make this fully API based.</p>
<p>To make this MUCH faster I will use the <a href="https://github.com/huggingface/text-embeddings-inference">Text Embeddings Inference</a> image. This has many benefits like: - No model graph compilation step - Small docker images and fast boot times. Get ready for true serverless! - Token based dynamic batching - Optimized transformers code for inference using Flash Attention, Candle and cuBLASLt - Safetensors weight loading - Production ready (distributed tracing with Open Telemetry, Prometheus metrics)</p>
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img src="https://media.githubusercontent.com/media/huggingface/text-embeddings-inference/main/assets/bs1-tp.png" class="img-fluid figure-img"></p>
<figcaption>img</figcaption>
</figure>
</div>
</section>
<section id="requirements" class="level2">
<h2 class="anchored" data-anchor-id="requirements">Requirements</h2>
<div id="35386f72-32cb-49fa-a108-3aa504e20429" class="cell" data-tags="[]">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="op">-</span>q aiohttp<span class="op">==</span><span class="fl">3.8.3</span> datasets<span class="op">==</span><span class="fl">2.14.6</span> pandas<span class="op">==</span><span class="fl">1.5.3</span> requests<span class="op">==</span><span class="fl">2.31.0</span> tqdm<span class="op">==</span><span class="fl">4.66.1</span> huggingface<span class="op">-</span>hub<span class="op">&gt;=</span><span class="fl">0.20</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="imports" class="level2">
<h2 class="anchored" data-anchor-id="imports">Imports</h2>
<div id="e2beecdd-d033-4736-bd45-6754ec53b4ac" class="cell" data-tags="[]" data-execution_count="3">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> asyncio</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> getpass <span class="im">import</span> getpass</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> json</span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pathlib <span class="im">import</span> Path</span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> time</span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> typing <span class="im">import</span> Optional</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> aiohttp <span class="im">import</span> ClientSession, ClientTimeout</span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> datasets <span class="im">import</span> load_dataset, Dataset, DatasetDict</span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> huggingface_hub <span class="im">import</span> notebook_login, create_inference_endpoint, list_inference_endpoints, whoami</span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> requests</span>
<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tqdm.auto <span class="im">import</span> tqdm</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="config" class="level2">
<h2 class="anchored" data-anchor-id="config">Config</h2>
<p><code>DATASET_IN</code> is where your text data is <code>DATASET_OUT</code> is where your embeddings will be stored</p>
<p>Note I used 5 for the <code>MAX_WORKERS</code> since <code>jina-embeddings-v2</code> are quite memory hungry.</p>
<div id="df2f79f0-9f28-46e6-9fc7-27e9537ff5be" class="cell" data-tags="[]" data-execution_count="4">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>DATASET_IN <span class="op">=</span> <span class="st">'derek-thomas/dataset-creator-reddit-bestofredditorupdates'</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>DATASET_OUT <span class="op">=</span> <span class="st">"processed-subset-bestofredditorupdates"</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>ENDPOINT_NAME <span class="op">=</span> <span class="st">"boru-jina-embeddings-demo-ie"</span></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>MAX_WORKERS <span class="op">=</span> <span class="dv">5</span>  <span class="co"># This is for how many async workers you want. Choose based on the model and hardware </span></span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>ROW_COUNT <span class="op">=</span> <span class="dv">100</span>  <span class="co"># Choose None to use all rows, Im using 100 just for a demo</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Hugging Face offers a number of GPUs that you can choose from a number of GPUs that you can choose in Inference Endpoints. Here they are in table form:</p>
<table class="caption-top table">
<thead>
<tr class="header">
<th>GPU</th>
<th>instanceType</th>
<th>instanceSize</th>
<th>vRAM</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>1x Nvidia Tesla T4</td>
<td>g4dn.xlarge</td>
<td>small</td>
<td>16GB</td>
</tr>
<tr class="even">
<td>4x Nvidia Tesla T4</td>
<td>g4dn.12xlarge</td>
<td>large</td>
<td>64GB</td>
</tr>
<tr class="odd">
<td>1x Nvidia A10G</td>
<td>g5.2xlarge</td>
<td>medium</td>
<td>24GB</td>
</tr>
<tr class="even">
<td>4x Nvidia A10G</td>
<td>g5.12xlarge</td>
<td>xxlarge</td>
<td>96GB</td>
</tr>
<tr class="odd">
<td>1x Nvidia A100*</td>
<td>p4de</td>
<td>xlarge</td>
<td>80GB</td>
</tr>
<tr class="even">
<td>2x Nvidia A100*</td>
<td>p4de</td>
<td>2xlarge</td>
<td>160GB</td>
</tr>
</tbody>
</table>
<p>*Note that for A100s you might get a note to email us to get access.</p>
<div id="3c2106c1-2e5a-443a-9ea8-a3cd0e9c5a94" class="cell" data-tags="[]" data-execution_count="4">
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># GPU Choice</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>VENDOR<span class="op">=</span><span class="st">"aws"</span></span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>REGION<span class="op">=</span><span class="st">"us-east-1"</span></span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>INSTANCE_SIZE<span class="op">=</span><span class="st">"medium"</span></span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>INSTANCE_TYPE<span class="op">=</span><span class="st">"g5.2xlarge"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div id="0ca1140c-3fcc-4b99-9210-6da1505a27b7" class="cell" data-tags="[]" data-execution_count="5">
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>notebook_login()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-display">
<script type="application/vnd.jupyter.widget-view+json">
{"model_id":"ee80821056e147fa9cabf30f64dc85a8","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
</script>
</div>
</div>
<p>Some users might have payment registered in an organization. This allows you to connect to an organization (that you are a member of) with a payment method.</p>
<p>Leave it blank is you want to use your username.</p>
<div id="88cdbd73-5923-4ae9-9940-b6be935f70fa" class="cell" data-tags="[]" data-execution_count="6">
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>who <span class="op">=</span> whoami()</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>organization <span class="op">=</span> getpass(prompt<span class="op">=</span><span class="st">"What is your Hugging Face 🤗 username or organization? (with an added payment method)"</span>)</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>namespace <span class="op">=</span> organization <span class="kw">or</span> who[<span class="st">'name'</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>What is your Hugging Face 🤗 username or organization? (with an added payment method) ········</code></pre>
</div>
</div>
</section>
<section id="get-dataset" class="level2">
<h2 class="anchored" data-anchor-id="get-dataset">Get Dataset</h2>
<div id="27835fa4-3a4f-44b1-a02a-5e31584a1bba" class="cell" data-tags="[]" data-execution_count="7">
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> load_dataset(DATASET_IN)</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>dataset[<span class="st">'train'</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-display">
<script type="application/vnd.jupyter.widget-view+json">
{"model_id":"4041cedd3b3f4f8db3e29ec102f46a3a","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
</script>
</div>
<div class="cell-output cell-output-display" data-execution_count="7">
<pre><code>Dataset({
    features: ['id', 'content', 'score', 'date_utc', 'title', 'flair', 'poster', 'permalink', 'new', 'updated'],
    num_rows: 10042
})</code></pre>
</div>
</div>
<div id="8846087e-4d0d-4c0e-8aeb-ea95d9e97126" class="cell" data-tags="[]" data-execution_count="8">
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>documents <span class="op">=</span> dataset[<span class="st">'train'</span>].to_pandas().to_dict(<span class="st">'records'</span>)[:ROW_COUNT]</span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="bu">len</span>(documents), documents[<span class="dv">0</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-display" data-execution_count="8">
<pre><code>(100,
 {'id': '10004zw',
  'content': '[removed]',
  'score': 1,
  'date_utc': Timestamp('2022-12-31 18:16:22'),
  'title': 'To All BORU contributors, Thank you :)',
  'flair': 'CONCLUDED',
  'poster': 'IsItAcOnSeQuEnCe',
  'permalink': '/r/BestofRedditorUpdates/comments/10004zw/to_all_boru_contributors_thank_you/',
  'new': False,
  'updated': False})</code></pre>
</div>
</div>
</section>
</section>
<section id="inference-endpoints" class="level1">
<h1>Inference Endpoints</h1>
<section id="create-inference-endpoint" class="level2">
<h2 class="anchored" data-anchor-id="create-inference-endpoint">Create Inference Endpoint</h2>
<p>We are going to use the <a href="https://huggingface.co./docs/inference-endpoints/api_reference">API</a> to create an <a href="https://huggingface.co./inference-endpoints">Inference Endpoint</a>. This should provide a few main benefits: - It’s convenient (No clicking) - It’s repeatable (We have the code to run it easily) - It’s cheaper (No time spent waiting for it to load, and automatically shut it down)</p>
<div id="9e59de46-26b7-4bb9-bbad-8bba9931bde7" class="cell" data-tags="[]" data-execution_count="9">
<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="cf">try</span>:</span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>    endpoint <span class="op">=</span> create_inference_endpoint(</span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>        ENDPOINT_NAME,</span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>        repository<span class="op">=</span><span class="st">"jinaai/jina-embeddings-v2-base-en"</span>,</span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a>        revision<span class="op">=</span><span class="st">"7302ac470bed880590f9344bfeee32ff8722d0e5"</span>,</span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a>        task<span class="op">=</span><span class="st">"sentence-embeddings"</span>,</span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a>        framework<span class="op">=</span><span class="st">"pytorch"</span>,</span>
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a>        accelerator<span class="op">=</span><span class="st">"gpu"</span>,</span>
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a>        instance_size<span class="op">=</span>INSTANCE_SIZE,</span>
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a>        instance_type<span class="op">=</span>INSTANCE_TYPE,</span>
<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a>        region<span class="op">=</span>REGION,</span>
<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a>        vendor<span class="op">=</span>VENDOR,</span>
<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>        namespace<span class="op">=</span>namespace,</span>
<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a>        custom_image<span class="op">=</span>{</span>
<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a>            <span class="st">"health_route"</span>: <span class="st">"/health"</span>,</span>
<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a>            <span class="st">"env"</span>: {</span>
<span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a>                <span class="st">"MAX_BATCH_TOKENS"</span>: <span class="bu">str</span>(MAX_WORKERS <span class="op">*</span> <span class="dv">2048</span>),</span>
<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a>                <span class="st">"MAX_CONCURRENT_REQUESTS"</span>: <span class="st">"512"</span>,</span>
<span id="cb12-19"><a href="#cb12-19" aria-hidden="true" tabindex="-1"></a>                <span class="st">"MODEL_ID"</span>: <span class="st">"/repository"</span></span>
<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a>            },</span>
<span id="cb12-21"><a href="#cb12-21" aria-hidden="true" tabindex="-1"></a>            <span class="st">"url"</span>: <span class="st">"ghcr.io/huggingface/text-embeddings-inference:0.5.0"</span>,</span>
<span id="cb12-22"><a href="#cb12-22" aria-hidden="true" tabindex="-1"></a>        },</span>
<span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a>        <span class="bu">type</span><span class="op">=</span><span class="st">"protected"</span>,</span>
<span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a>    )</span>
<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a><span class="cf">except</span>:</span>
<span id="cb12-26"><a href="#cb12-26" aria-hidden="true" tabindex="-1"></a>    endpoint <span class="op">=</span> [ie <span class="cf">for</span> ie <span class="kw">in</span> list_inference_endpoints(namespace<span class="op">=</span>namespace) <span class="cf">if</span> ie.name <span class="op">==</span> ENDPOINT_NAME][<span class="dv">0</span>]</span>
<span id="cb12-27"><a href="#cb12-27" aria-hidden="true" tabindex="-1"></a>    <span class="bu">print</span>(<span class="st">'Loaded endpoint'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>There are a few design choices here: - As discussed before we are using <code>jinaai/jina-embeddings-v2-base-en</code> as our model. - For reproducibility we are pinning it to a specific revision. - If you are interested in more models, check out the supported list <a href="https://huggingface.co./docs/text-embeddings-inference/supported_models">here</a>. - Note that most embedding models are based on the BERT architecture. - <code>MAX_BATCH_TOKENS</code> is chosen based on our number of workers and the context window of our embedding model. - <code>type="protected"</code> utilized the security from Inference Endpoints detailed here. - I’m using <strong>1x Nvidia A10</strong> since <code>jina-embeddings-v2</code> is memory hungry (remember the 8k context length). - You should consider further tuning <code>MAX_BATCH_TOKENS</code> and <code>MAX_CONCURRENT_REQUESTS</code> if you have high workloads</p>
</section>
<section id="wait-until-its-running" class="level2">
<h2 class="anchored" data-anchor-id="wait-until-its-running">Wait until it’s running</h2>
<div id="5f3a8bd2-753c-49a8-9452-899578beddc5" class="cell" data-tags="[]" data-execution_count="10">
<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>time</span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>endpoint.wait()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>CPU times: user 48.1 ms, sys: 15.7 ms, total: 63.8 ms
Wall time: 52.6 s</code></pre>
</div>
<div class="cell-output cell-output-display" data-execution_count="10">
<pre><code>InferenceEndpoint(name='boru-jina-embeddings-demo-ie', namespace='HF-test-lab', repository='jinaai/jina-embeddings-v2-base-en', status='running', url='https://k7l1xeok1jwnpbx5.us-east-1.aws.endpoints.huggingface.cloud')</code></pre>
</div>
</div>
<p>When we use <code>endpoint.client.post</code> we get a bytes string back. This is a little tedious because we need to convert this to an <code>np.array</code>, but it’s just a couple quick lines in python.</p>
<div id="e09253d5-70ff-4d0e-8888-0022ce0adf7b" class="cell" data-tags="[]" data-execution_count="12">
<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>response <span class="op">=</span> endpoint.client.post(json<span class="op">=</span>{<span class="st">"inputs"</span>: <span class="st">'This sound track was beautiful! It paints the senery in your mind so well I would recomend it even to people who hate vid. game music!'</span>, <span class="st">'truncate'</span>: <span class="va">True</span>}, task<span class="op">=</span><span class="st">"feature-extraction"</span>)</span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>response <span class="op">=</span> np.array(json.loads(response.decode()))</span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>response[<span class="dv">0</span>][:<span class="dv">20</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-display" data-execution_count="12">
<pre><code>array([-0.05630935, -0.03560849,  0.02789049,  0.02792823, -0.02800371,
       -0.01530391, -0.01863454, -0.0077982 ,  0.05374297,  0.03672185,
       -0.06114018, -0.06880157, -0.0093503 , -0.03174005, -0.03206085,
        0.0610647 ,  0.02243694,  0.03217408,  0.04181686,  0.00248854])</code></pre>
</div>
</div>
<p>You may have inputs that exceed the context. In such scenarios, it’s up to you to handle them. In my case, I’d like to truncate rather than have an error. Let’s test that it works.</p>
<div id="a4a1cd15-dda3-4cfa-8bda-788d8c1b9e32" class="cell" data-tags="[]" data-execution_count="13">
<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>embedding_input <span class="op">=</span> <span class="st">'This input will get multiplied'</span> <span class="op">*</span> <span class="dv">10000</span></span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f'The length of the embedding_input is: </span><span class="sc">{</span><span class="bu">len</span>(embedding_input)<span class="sc">}</span><span class="ss">'</span>)</span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>response <span class="op">=</span> endpoint.client.post(json<span class="op">=</span>{<span class="st">"inputs"</span>: embedding_input, <span class="st">'truncate'</span>: <span class="va">True</span>}, task<span class="op">=</span><span class="st">"feature-extraction"</span>)</span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>response <span class="op">=</span> np.array(json.loads(response.decode()))</span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>response[<span class="dv">0</span>][:<span class="dv">20</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>The length of the embedding_input is: 300000</code></pre>
</div>
<div class="cell-output cell-output-display" data-execution_count="13">
<pre><code>array([-0.03088215, -0.0351537 ,  0.05749275,  0.00983467,  0.02108356,
        0.04539965,  0.06107162, -0.02536954,  0.03887688,  0.01998681,
       -0.05391388,  0.01529677, -0.1279156 ,  0.01653782, -0.01940958,
        0.0367411 ,  0.0031748 ,  0.04716022, -0.00713609, -0.00155313])</code></pre>
</div>
</div>
</section>
</section>
<section id="get-embeddings" class="level1">
<h1>Get Embeddings</h1>
<p>Here I send a document, update it with the embedding, and return it. This happens in parallel with <code>MAX_WORKERS</code>.</p>
<div id="ad3193fb-3def-42a8-968e-c63f2b864ca8" class="cell" data-tags="[]" data-execution_count="14">
<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="cf">async</span> <span class="kw">def</span> request(document, semaphore):</span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Semaphore guard</span></span>
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">async</span> <span class="cf">with</span> semaphore:</span>
<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>        result <span class="op">=</span> <span class="cf">await</span> endpoint.async_client.post(json<span class="op">=</span>{<span class="st">"inputs"</span>: document[<span class="st">'content'</span>], <span class="st">'truncate'</span>: <span class="va">True</span>}, task<span class="op">=</span><span class="st">"feature-extraction"</span>)</span>
<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a>        result <span class="op">=</span> np.array(json.loads(result.decode()))</span>
<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a>        document[<span class="st">'embedding'</span>] <span class="op">=</span> result[<span class="dv">0</span>]  <span class="co"># Assuming the API's output can be directly assigned</span></span>
<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a>        <span class="cf">return</span> document</span>
<span id="cb21-8"><a href="#cb21-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-9"><a href="#cb21-9" aria-hidden="true" tabindex="-1"></a><span class="cf">async</span> <span class="kw">def</span> main(documents):</span>
<span id="cb21-10"><a href="#cb21-10" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Semaphore to limit concurrent requests. Adjust the number as needed.</span></span>
<span id="cb21-11"><a href="#cb21-11" aria-hidden="true" tabindex="-1"></a>    semaphore <span class="op">=</span> asyncio.BoundedSemaphore(MAX_WORKERS)</span>
<span id="cb21-12"><a href="#cb21-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-13"><a href="#cb21-13" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Creating a list of tasks</span></span>
<span id="cb21-14"><a href="#cb21-14" aria-hidden="true" tabindex="-1"></a>    tasks <span class="op">=</span> [request(document, semaphore) <span class="cf">for</span> document <span class="kw">in</span> documents]</span>
<span id="cb21-15"><a href="#cb21-15" aria-hidden="true" tabindex="-1"></a>    </span>
<span id="cb21-16"><a href="#cb21-16" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Using tqdm to show progress. It's been integrated into the async loop.</span></span>
<span id="cb21-17"><a href="#cb21-17" aria-hidden="true" tabindex="-1"></a>    <span class="cf">for</span> f <span class="kw">in</span> tqdm(asyncio.as_completed(tasks), total<span class="op">=</span><span class="bu">len</span>(documents)):</span>
<span id="cb21-18"><a href="#cb21-18" aria-hidden="true" tabindex="-1"></a>        <span class="cf">await</span> f</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div id="ec4983af-65eb-4841-808a-3738fb4d682d" class="cell" data-tags="[]" data-execution_count="15">
<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>start <span class="op">=</span> time.perf_counter()</span>
<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Get embeddings</span></span>
<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a><span class="cf">await</span> main(documents)</span>
<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a><span class="co"># Make sure we got it all</span></span>
<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a>count <span class="op">=</span> <span class="dv">0</span></span>
<span id="cb22-8"><a href="#cb22-8" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> document <span class="kw">in</span> documents:</span>
<span id="cb22-9"><a href="#cb22-9" aria-hidden="true" tabindex="-1"></a>    <span class="cf">if</span> <span class="st">'embedding'</span> <span class="kw">in</span> document.keys() <span class="kw">and</span> <span class="bu">len</span>(document[<span class="st">'embedding'</span>]) <span class="op">==</span> <span class="dv">768</span>:</span>
<span id="cb22-10"><a href="#cb22-10" aria-hidden="true" tabindex="-1"></a>        count <span class="op">+=</span> <span class="dv">1</span></span>
<span id="cb22-11"><a href="#cb22-11" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f'Embeddings = </span><span class="sc">{</span>count<span class="sc">}</span><span class="ss"> documents = </span><span class="sc">{</span><span class="bu">len</span>(documents)<span class="sc">}</span><span class="ss">'</span>)</span>
<span id="cb22-12"><a href="#cb22-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-13"><a href="#cb22-13" aria-hidden="true" tabindex="-1"></a>            </span>
<span id="cb22-14"><a href="#cb22-14" aria-hidden="true" tabindex="-1"></a><span class="co"># Print elapsed time</span></span>
<span id="cb22-15"><a href="#cb22-15" aria-hidden="true" tabindex="-1"></a>elapsed_time <span class="op">=</span> time.perf_counter() <span class="op">-</span> start</span>
<span id="cb22-16"><a href="#cb22-16" aria-hidden="true" tabindex="-1"></a>minutes, seconds <span class="op">=</span> <span class="bu">divmod</span>(elapsed_time, <span class="dv">60</span>)</span>
<span id="cb22-17"><a href="#cb22-17" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"</span><span class="sc">{</span><span class="bu">int</span>(minutes)<span class="sc">}</span><span class="ss"> min </span><span class="sc">{</span>seconds<span class="sc">:.2f}</span><span class="ss"> sec"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<div class="cell-output cell-output-stdout">
<pre><code>Embeddings = 100 documents = 100
0 min 21.33 sec</code></pre>
</div>
</div>
<section id="pause-inference-endpoint" class="level2">
<h2 class="anchored" data-anchor-id="pause-inference-endpoint">Pause Inference Endpoint</h2>
<p>Now that we have finished, let’s pause the endpoint so we don’t incur any extra charges, this will also allow us to analyze the cost.</p>
<div id="540a0978-7670-4ce3-95c1-3823cc113b85" class="cell" data-tags="[]" data-execution_count="16">
<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>endpoint <span class="op">=</span> endpoint.pause()</span>
<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"Endpoint Status: </span><span class="sc">{</span>endpoint<span class="sc">.</span>status<span class="sc">}</span><span class="ss">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Endpoint Status: paused</code></pre>
</div>
</div>
</section>
</section>
<section id="push-updated-dataset-to-hub" class="level1">
<h1>Push updated dataset to Hub</h1>
<p>We now have our documents updated with the embeddings we wanted. First we need to convert it back to a <code>Dataset</code> format. I find it easiest to go from list of dicts -&gt; <code>pd.DataFrame</code> -&gt; <code>Dataset</code></p>
<div id="9bb993f8-d624-4192-9626-8e9ed9888a1b" class="cell" data-tags="[]" data-execution_count="17">
<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(documents)</span>
<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>dd <span class="op">=</span> DatasetDict({<span class="st">'train'</span>: Dataset.from_pandas(df)})</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>I’m uploading it to the user’s account by default (as opposed to uploading to an organization) but feel free to push to wherever you want by setting the user in the <code>repo_id</code> or in the config by setting <code>DATASET_OUT</code></p>
<div id="f48e7c55-d5b7-4ed6-8516-272ae38716b1" class="cell" data-tags="[]" data-execution_count="18">
<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>dd.push_to_hub(repo_id<span class="op">=</span>DATASET_OUT)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<div id="85ea2244-a4c6-4f04-b187-965a2fc356a8" class="cell" data-tags="[]" data-execution_count="19">
<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f'Dataset is at https://huggingface.co./datasets/</span><span class="sc">{</span>who[<span class="st">"name"</span>]<span class="sc">}</span><span class="ss">/</span><span class="sc">{</span>DATASET_OUT<span class="sc">}</span><span class="ss">'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Dataset is at https://huggingface.co./datasets/derek-thomas/processed-subset-bestofredditorupdates</code></pre>
</div>
</div>
</section>
<section id="analyze-usage" class="level1">
<h1>Analyze Usage</h1>
<ol type="1">
<li>Go to your <code>dashboard_url</code> printed below</li>
<li>Click on the Usage &amp; Cost tab</li>
<li>See how much you have spent</li>
</ol>
<div id="16815445-3079-43da-b14e-b54176a07a62" class="cell" data-execution_count="20">
<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>dashboard_url <span class="op">=</span> <span class="ss">f'https://ui.endpoints.huggingface.co/</span><span class="sc">{</span>namespace<span class="sc">}</span><span class="ss">/endpoints/</span><span class="sc">{</span>ENDPOINT_NAME<span class="sc">}</span><span class="ss">'</span></span>
<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(dashboard_url)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>https://ui.endpoints.huggingface.co/HF-test-lab/endpoints/boru-jina-embeddings-demo-ie</code></pre>
</div>
</div>
<div id="81096c6f-d12f-4781-84ec-9066cfa465b3" class="cell" data-execution_count="21">
<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="bu">input</span>(<span class="st">"Hit enter to continue with the notebook"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Hit enter to continue with the notebook </code></pre>
</div>
<div class="cell-output cell-output-display" data-execution_count="21">
<pre><code>''</code></pre>
</div>
</div>
<p>We can see that it only took <code>$0.04</code> to pay for this!</p>
</section>
<section id="delete-endpoint" class="level1">
<h1>Delete Endpoint</h1>
<p>Now that we are done, we don’t need our endpoint anymore. We can delete our endpoint programmatically.</p>
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img src="https://huggingface.co./datasets/huggingface/cookbook-images/resolve/main/automatic_embedding_tei_inference_endpoints.png" class="img-fluid figure-img"></p>
<figcaption>Cost</figcaption>
</figure>
</div>
<div id="c310c0f3-6f12-4d5c-838b-3a4c1f2e54ad" class="cell" data-tags="[]" data-execution_count="22">
<div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>endpoint <span class="op">=</span> endpoint.delete()</span>
<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> <span class="kw">not</span> endpoint:</span>
<span id="cb35-4"><a href="#cb35-4" aria-hidden="true" tabindex="-1"></a>    <span class="bu">print</span>(<span class="st">'Endpoint deleted successfully'</span>)</span>
<span id="cb35-5"><a href="#cb35-5" aria-hidden="true" tabindex="-1"></a><span class="cf">else</span>:</span>
<span id="cb35-6"><a href="#cb35-6" aria-hidden="true" tabindex="-1"></a>    <span class="bu">print</span>(<span class="st">'Delete Endpoint in manually'</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Endpoint deleted successfully</code></pre>
</div>
</div>


</section>

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