message: Update hugectr_user_guide.md
  Fix the invalid link
diff: @@ -161,7 +161,7 @@ Please find more information under [Python Interface Introduction](./python_inte
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  ### Embedding Training Cache ###
  -Embedding Training Cache (Model oversubscription) gives you the ability to train a large model up to TeraBytes. It's implemented by loading a subset of an embedding table, which exceeds the aggregated capacity of GPU's memory, into the GPU in a coarse-grained, on-demand manner during the training stage. To use this feature, you need to split your dataset into multiple sub-datasets while extracting the unique key sets from them (see Fig. 7).<br/>This feature currently supports both single and multi-node training. It supports all embedding types and can be used with [Norm](./python_interface.md#norm) and [Raw](./python_interface.md#raw) dataset formats. We revised our [`criteo2hugectr` tool](../tools/criteo_script/criteo2hugectr.cpp) to support the key set extraction for the Criteo dataset. For additional information, see our [Python Jupyter Notebook](../notebooks/python_interface.ipynb) to learn how to use this feature with the Criteo dataset. Please note that The Criteo dataset is a common use case, but model prefetching is not limited to this dataset.
  +Embedding Training Cache (Model oversubscription) gives you the ability to train a large model up to TeraBytes. It's implemented by loading a subset of an embedding table, which exceeds the aggregated capacity of GPU's memory, into the GPU in a coarse-grained, on-demand manner during the training stage. To use this feature, you need to split your dataset into multiple sub-datasets while extracting the unique key sets from them (see Fig. 7).<br/>This feature currently supports both single and multi-node training. It supports all embedding types and can be used with [Norm](./python_interface.md#norm) and [Raw](./python_interface.md#raw) dataset formats. We revised our [`criteo2hugectr` tool](../tools/criteo_script/criteo2hugectr.cpp) to support the key set extraction for the Criteo dataset. For additional information, see our [Python Jupyter Notebook](../notebooks/hugectr_criteo.ipynb) to learn how to use this feature with the Criteo dataset. Please note that The Criteo dataset is a common use case, but model prefetching is not limited to this dataset.
  <div align=center><img width="520" height="153" src="user_guide_src/dataset_split.png"/></div>
  <div align=center>Fig. 7: Preprocessing of dataset for model oversubscription</div>
  