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‎courses/ray-101/1_AI_Libs_Intro.html‎

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<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
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<span class="ne">ModuleNotFoundError</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
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<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
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<span class="ne">----&gt; </span><span class="mi">1</span> <span class="kn">import</span><span class="w"> </span><span class="nn">ray</span>
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<span class="g g-Whitespace"> </span><span class="mi">3</span> <span class="c1"># Read the dataset from S3 (Parquet file)</span>
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<span class="g g-Whitespace"> </span><span class="mi">4</span> <span class="n">dataset</span> <span class="o">=</span> <span class="n">ray</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">&quot;s3://anonymous@anyscale-training-data/intro-to-ray-air/nyc_taxi_2021.parquet&quot;</span><span class="p">)</span>
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<span class="ne">ModuleNotFoundError</span>: No module named &#39;ray&#39;
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</pre></div>
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<script type="application/vnd.jupyter.widget-view+json">{"model_id": "4171837fd1c94e5882794ad391d8c1fc", "version_major": 2, "version_minor": 0}</script></div>
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-11-15 15:50:21,044 INFO streaming_executor.py:108 -- Starting execution of Dataset. Full logs are in /tmp/ray/session_2024-11-15_08-44-25_924022_2383/logs/ray-data
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2024-11-15 15:50:21,044 INFO streaming_executor.py:109 -- Execution plan of Dataset: InputDataBuffer[Input] -&gt; TaskPoolMapOperator[ReadParquet]
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</pre></div>
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<script type="application/vnd.jupyter.widget-view+json">{"model_id": "4b6bb7e909d14bc882d43009e78efcd2", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "479f8e44c62544feb87819a1fdc8fc7f", "version_major": 2, "version_minor": 0}</script><div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-11-15 15:50:21,913 INFO streaming_executor.py:108 -- Starting execution of Dataset. Full logs are in /tmp/ray/session_2024-11-15_08-44-25_924022_2383/logs/ray-data
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2024-11-15 15:50:21,913 INFO streaming_executor.py:109 -- Execution plan of Dataset: InputDataBuffer[Input] -&gt; TaskPoolMapOperator[ReadParquet]
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</pre></div>
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<script type="application/vnd.jupyter.widget-view+json">{"model_id": "6df515a45ff042f18618c9a8de31a32b", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "69d8b5498be1439eb75aebeb29bef4df", "version_major": 2, "version_minor": 0}</script></div>
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<script type="application/vnd.jupyter.widget-view+json">{"model_id": "4b6bb7e909d14bc882d43009e78efcd2", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "479f8e44c62544feb87819a1fdc8fc7f", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "6df515a45ff042f18618c9a8de31a32b", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "69d8b5498be1439eb75aebeb29bef4df", "version_major": 2, "version_minor": 0}</script></div>
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<p><strong>Fit model with Ray Train</strong></p>
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-11-15 15:54:19,191 INFO tune.py:616 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949
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2024-11-15 15:54:19,206 INFO data_parallel_trainer.py:340 -- GPUs are detected in your Ray cluster, but GPU training is not enabled for this trainer. To enable GPU training, make sure to set `use_gpu` to True in your scaling config.
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>== Status ==
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Current time: 2024-11-15 15:54:19 (running for 00:00:00.11)
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Using FIFO scheduling algorithm.
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Number of trials: 1/1 (1 RUNNING)
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-11-15 15:54:31,784 INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to &#39;/mnt/cluster_storage/XGBoostTrainer_2024-11-15_15-54-19&#39; in 0.0222s.
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2024-11-15 15:54:31,786 INFO tune.py:1041 -- Total run time: 12.60 seconds (12.56 seconds for the tuning loop).
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>== Status ==
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Current time: 2024-11-15 15:54:31 (running for 00:00:12.58)
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margin: 0.5em 1em 0.5em 1em;
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</div><div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-11-15 15:58:50,120 INFO data_parallel_trainer.py:340 -- GPUs are detected in your Ray cluster, but GPU training is not enabled for this trainer. To enable GPU training, make sure to set `use_gpu` to True in your scaling config.
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2024-11-15 15:58:50,123 INFO data_parallel_trainer.py:340 -- GPUs are detected in your Ray cluster, but GPU training is not enabled for this trainer. To enable GPU training, make sure to set `use_gpu` to True in your scaling config.
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2024-11-15 15:58:50,127 INFO data_parallel_trainer.py:340 -- GPUs are detected in your Ray cluster, but GPU training is not enabled for this trainer. To enable GPU training, make sure to set `use_gpu` to True in your scaling config.
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<script type="application/vnd.jupyter.widget-view+json">{"model_id": "30f9e22dd4784d80863e3781805052f8", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "6398e9e2df08452c882c4ab105d49868", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "7bd78ce7678b4e6fa53f99b1e09bd3e4", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "806ccd0b5ec94ef98c0e3e603fb049f7", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "21e450d69d49426aad1eff31652a5c52", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "66f05ffd807747b1aa5ec75c41f1a219", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "77c870497eac42629f915ac6bac6ad8c", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "7f9c7bc17b9b43589f9816889d615081", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "438969ea3e094c59a13a4e4b66adb21c", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "9566d31c0d45409d8069531b559e4549", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "eee51de61b9143b4ae68896f1ec29ad6", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "da60c29650ed44feb38760e048a9f123", "version_major": 2, "version_minor": 0}</script><div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-11-15 15:59:03,410 INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to &#39;/mnt/cluster_storage/XGBoostTrainer_2024-11-15_15-58-50&#39; in 0.0516s.
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2024-11-15 15:59:03,415 INFO tune.py:1041 -- Total run time: 13.31 seconds (13.24 seconds for the tuning loop).
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</div><script type="application/vnd.jupyter.widget-view+json">{"model_id": "30f9e22dd4784d80863e3781805052f8", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "6398e9e2df08452c882c4ab105d49868", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "7bd78ce7678b4e6fa53f99b1e09bd3e4", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "806ccd0b5ec94ef98c0e3e603fb049f7", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "21e450d69d49426aad1eff31652a5c52", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "66f05ffd807747b1aa5ec75c41f1a219", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "77c870497eac42629f915ac6bac6ad8c", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "7f9c7bc17b9b43589f9816889d615081", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "438969ea3e094c59a13a4e4b66adb21c", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "9566d31c0d45409d8069531b559e4549", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "eee51de61b9143b4ae68896f1ec29ad6", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "da60c29650ed44feb38760e048a9f123", "version_major": 2, "version_minor": 0}</script></div>
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<p><strong>Batch inference with Ray Data</strong></p>
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2024-11-15 16:05:08,460 INFO streaming_executor.py:109 -- Execution plan of Dataset: InputDataBuffer[Input] -&gt; ActorPoolMapOperator[MapBatches(drop_columns)-&gt;MapBatches(OfflinePredictor)] -&gt; LimitOperator[limit=10]
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<script type="application/vnd.jupyter.widget-view+json">{"model_id": "020cdc52c156498d93d5ac50905882c2", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "efd4d96ee39c4be0b4c05fc233aaaa35", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "7349b3f1ba794b08a66b8b855489625a", "version_major": 2, "version_minor": 0}</script><div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-11-15 16:05:10,388 WARNING actor_pool_map_operator.py:265 -- To ensure full parallelization across an actor pool of size 2, the Dataset should consist of at least 2 distinct blocks. Consider increasing the parallelism when creating the Dataset.
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;prediction&#39;: array([0.6290559 , 0.6290559 , 0.5404948 , 0.6290559 , 0.5552995 ,
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<script type="application/vnd.jupyter.widget-view+json">{"model_id": "020cdc52c156498d93d5ac50905882c2", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "efd4d96ee39c4be0b4c05fc233aaaa35", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "7349b3f1ba794b08a66b8b855489625a", "version_major": 2, "version_minor": 0}</script><div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;prediction&#39;: array([0.6290559 , 0.6290559 , 0.5404948 , 0.6290559 , 0.5552995 ,
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-11-15 16:08:47,352 WARNING api.py:346 -- The default value for `max_ongoing_requests` has changed from 100 to 5 in Ray 2.32.0.
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2024-11-15 16:08:47,354 WARNING api.py:397 -- The default value for `max_ongoing_requests` has changed from 100 to 5 in Ray 2.32.0.
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2024-11-15 16:08:51,404 INFO handle.py:126 -- Created DeploymentHandle &#39;dx5qijjx&#39; for Deployment(name=&#39;OnlinePredictor&#39;, app=&#39;default&#39;).
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2024-11-15 16:08:51,405 INFO handle.py:126 -- Created DeploymentHandle &#39;bw4198ni&#39; for Deployment(name=&#39;OnlinePredictor&#39;, app=&#39;default&#39;).
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2024-11-15 16:08:54,422 INFO handle.py:126 -- Created DeploymentHandle &#39;es3qaucg&#39; for Deployment(name=&#39;OnlinePredictor&#39;, app=&#39;default&#39;).
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2024-11-15 16:08:54,423 INFO api.py:574 -- Deployed app &#39;default&#39; successfully.
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2024-11-15 16:09:49,192 INFO streaming_executor.py:109 -- Execution plan of Dataset: InputDataBuffer[Input] -&gt; TaskPoolMapOperator[MapBatches(drop_columns)] -&gt; LimitOperator[limit=1]
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<script type="application/vnd.jupyter.widget-view+json">{"model_id": "2678e03029204bb2ab5d53fcf1960566", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "b12bc1dd44f449e4ac518405338f7e5c", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "18f6b315401a4758920d699be2347224", "version_major": 2, "version_minor": 0}</script><div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;prediction&#39;: [0.629055917263031]}
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