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    <title>topic What's New:  Intel® Distribution for Python* 2025.1 in Intel® Distribution for Python*</title>
    <link>https://community.intel.com/t5/Intel-Distribution-for-Python/What-s-New-Intel-Distribution-for-Python-2025-1/m-p/1680192#M2247</link>
    <description>&lt;P&gt;The Intel® Distribution for Python, 2025.1 has been updated to include functional and security updates. Users should update to the latest version. For more details on security and known issues, please check out&amp;nbsp;&lt;A href="https://www.intel.com/content/www/us/en/developer/articles/troubleshooting/python-known-issues.html" target="_blank"&gt;Intel® Distribution for Python* Known Issues&lt;/A&gt;.&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;What’s new&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;EM&gt;Data Parallel Extension for Python*&lt;/EM&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;includes the following improvements:&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;Achieved 100% compliance with Python Array API specification (revision "2023.12").&lt;/LI&gt;
&lt;LI&gt;Added mathematical functions (&lt;SPAN class="code-simple"&gt;dpnp.gcd&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.i0&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.lcm&lt;/SPAN&gt;,&lt;SPAN class="code-simple"&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;dpnp.ldexp&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.sinc&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.spacing&lt;/SPAN&gt;).&lt;/LI&gt;
&lt;LI&gt;Added manipulation functions (&lt;SPAN class="code-simple"&gt;dpnp.delete&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.insert&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.pad&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.unstack&lt;/SPAN&gt;)&lt;/LI&gt;
&lt;LI&gt;Added statistics functions (&lt;SPAN class="code-simple"&gt;dpnp.bincount&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.corrcoef&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.correlate&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.histogram2d&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.histogramdd&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.nanmedian&lt;/SPAN&gt;).&lt;/LI&gt;
&lt;LI&gt;Added linear algebra functions (&lt;SPAN class="code-simple"&gt;dpnp.linalg.diagonal&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.cross&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg&lt;/SPAN&gt;.&lt;SPAN class="code-simple"&gt;matrix_norm&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.outer&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.tensordot&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.trace&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.svdvals&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.vecdot&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.vector_norm&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.matrix_transpose&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.matvec&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.vecmat&lt;/SPAN&gt;).&lt;/LI&gt;
&lt;LI&gt;Added indexing function&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.compress&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI&gt;Improved performance of&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.histogram&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.choose&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;and&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.nan_to_num functions&lt;/SPAN&gt;, and&lt;SPAN class="code-simple"&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;dpnp.ndarray.fill&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;method.&lt;/LI&gt;
&lt;LI&gt;Provided compatibility with NumPy 2.2.3.&lt;/LI&gt;
&lt;/UL&gt;
&lt;P&gt;&lt;BR /&gt;&lt;EM&gt;Data Parallel Control Library*&lt;/EM&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;includes the following improvements:&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;Added&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.top_k&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;function for getting the k smallest or largest elements, which will be coming to a future array API release.&lt;/LI&gt;
&lt;LI&gt;Added&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.dldevice_to_sycl_device,&lt;/SPAN&gt;&amp;nbsp;&lt;SPAN class="code-simple"&gt;tensor.sycl_device_to_dldevice,&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;and&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpctl.SyclDevice.get_device_id&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;methods to improve interoperability with DLPack.&lt;/LI&gt;
&lt;LI&gt;Improved performance of copy-and-cast operations for&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;numpy.ndarray&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;to&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.usm_ndarray&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;for contiguous inputs and for strided arrays to contiguous arrays.&lt;/LI&gt;
&lt;LI&gt;Improved performance of tensor accumulation functions (&lt;SPAN class="code-simple"&gt;tensor.cumulative_sum&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.cumulative_prod&lt;/SPAN&gt;, and&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.cumulative_logsumexp&lt;/SPAN&gt;).&lt;/LI&gt;
&lt;LI&gt;Improved performance of tensor reductions (&lt;SPAN class="code-simple"&gt;tensor.sum&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.max&lt;/SPAN&gt;, etc.)&lt;/LI&gt;
&lt;LI&gt;Improved performance of sort operations for some data types (especially small integral types) and added ‘radixsort’ sort kind to&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.sort&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;and&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.argsort.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;/UL&gt;</description>
    <pubDate>Thu, 03 Apr 2025 14:48:23 GMT</pubDate>
    <dc:creator>StefR_Intel</dc:creator>
    <dc:date>2025-04-03T14:48:23Z</dc:date>
    <item>
      <title>What's New:  Intel® Distribution for Python* 2025.1</title>
      <link>https://community.intel.com/t5/Intel-Distribution-for-Python/What-s-New-Intel-Distribution-for-Python-2025-1/m-p/1680192#M2247</link>
      <description>&lt;P&gt;The Intel® Distribution for Python, 2025.1 has been updated to include functional and security updates. Users should update to the latest version. For more details on security and known issues, please check out&amp;nbsp;&lt;A href="https://www.intel.com/content/www/us/en/developer/articles/troubleshooting/python-known-issues.html" target="_blank"&gt;Intel® Distribution for Python* Known Issues&lt;/A&gt;.&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;What’s new&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;EM&gt;Data Parallel Extension for Python*&lt;/EM&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;includes the following improvements:&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;Achieved 100% compliance with Python Array API specification (revision "2023.12").&lt;/LI&gt;
&lt;LI&gt;Added mathematical functions (&lt;SPAN class="code-simple"&gt;dpnp.gcd&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.i0&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.lcm&lt;/SPAN&gt;,&lt;SPAN class="code-simple"&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;dpnp.ldexp&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.sinc&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.spacing&lt;/SPAN&gt;).&lt;/LI&gt;
&lt;LI&gt;Added manipulation functions (&lt;SPAN class="code-simple"&gt;dpnp.delete&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.insert&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.pad&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.unstack&lt;/SPAN&gt;)&lt;/LI&gt;
&lt;LI&gt;Added statistics functions (&lt;SPAN class="code-simple"&gt;dpnp.bincount&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.corrcoef&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.correlate&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.histogram2d&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.histogramdd&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.nanmedian&lt;/SPAN&gt;).&lt;/LI&gt;
&lt;LI&gt;Added linear algebra functions (&lt;SPAN class="code-simple"&gt;dpnp.linalg.diagonal&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.cross&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg&lt;/SPAN&gt;.&lt;SPAN class="code-simple"&gt;matrix_norm&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.outer&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.tensordot&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.trace&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.svdvals&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.vecdot&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.linalg.vector_norm&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.matrix_transpose&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.matvec&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.vecmat&lt;/SPAN&gt;).&lt;/LI&gt;
&lt;LI&gt;Added indexing function&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.compress&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI&gt;Improved performance of&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.histogram&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.choose&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;and&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpnp.nan_to_num functions&lt;/SPAN&gt;, and&lt;SPAN class="code-simple"&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;dpnp.ndarray.fill&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;method.&lt;/LI&gt;
&lt;LI&gt;Provided compatibility with NumPy 2.2.3.&lt;/LI&gt;
&lt;/UL&gt;
&lt;P&gt;&lt;BR /&gt;&lt;EM&gt;Data Parallel Control Library*&lt;/EM&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;includes the following improvements:&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;Added&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.top_k&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;function for getting the k smallest or largest elements, which will be coming to a future array API release.&lt;/LI&gt;
&lt;LI&gt;Added&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.dldevice_to_sycl_device,&lt;/SPAN&gt;&amp;nbsp;&lt;SPAN class="code-simple"&gt;tensor.sycl_device_to_dldevice,&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;and&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;dpctl.SyclDevice.get_device_id&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;methods to improve interoperability with DLPack.&lt;/LI&gt;
&lt;LI&gt;Improved performance of copy-and-cast operations for&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;numpy.ndarray&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;to&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.usm_ndarray&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;for contiguous inputs and for strided arrays to contiguous arrays.&lt;/LI&gt;
&lt;LI&gt;Improved performance of tensor accumulation functions (&lt;SPAN class="code-simple"&gt;tensor.cumulative_sum&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.cumulative_prod&lt;/SPAN&gt;, and&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.cumulative_logsumexp&lt;/SPAN&gt;).&lt;/LI&gt;
&lt;LI&gt;Improved performance of tensor reductions (&lt;SPAN class="code-simple"&gt;tensor.sum&lt;/SPAN&gt;,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.max&lt;/SPAN&gt;, etc.)&lt;/LI&gt;
&lt;LI&gt;Improved performance of sort operations for some data types (especially small integral types) and added ‘radixsort’ sort kind to&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.sort&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;and&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class="code-simple"&gt;tensor.argsort.&lt;/SPAN&gt;&lt;/LI&gt;
&lt;/UL&gt;</description>
      <pubDate>Thu, 03 Apr 2025 14:48:23 GMT</pubDate>
      <guid>https://community.intel.com/t5/Intel-Distribution-for-Python/What-s-New-Intel-Distribution-for-Python-2025-1/m-p/1680192#M2247</guid>
      <dc:creator>StefR_Intel</dc:creator>
      <dc:date>2025-04-03T14:48:23Z</dc:date>
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