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Merge pull request #15 from oracle/2.6.6_docs
Update docs for distributed training
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‎docs/source/index.rst‎

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.. toctree::
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:hidden:
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:maxdepth: 5
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:caption: History:
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:caption: Getting Started:
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release_notes
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user_guide/quick_start/quick_start
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.. toctree::
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:hidden:
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:maxdepth: 5
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:caption: Installation and Configuration:
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user_guide/quick_start/quick_start
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user_guide/cli/quickstart
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user_guide/cli/authentication
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user_guide/cli/opctl/configure
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:caption: Tasks:
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user_guide/loading_data/connect
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user_guide/apachespark/spark
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user_guide/data_labeling/index
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user_guide/data_transformation/data_transformation
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user_guide/data_visualization/visualization
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user_guide/model_training/index
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user_guide/model_registration/introduction
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user_guide/ADSString/index
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.. toctree::
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:caption: Integrations:
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user_guide/apachespark/spark
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user_guide/big_data_service/index
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user_guide/jobs/index
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user_guide/logs/logs

‎docs/source/user_guide/ADSString/index.rst‎

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.. _ADSString:
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######################
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Manipulating Text Data
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######################
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TextStrings
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-----------
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regex_match
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still_a_string
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Text Extraction
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---------------
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../text_extraction/text_dataset

‎docs/source/user_guide/apachespark/spark.rst‎

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=========================
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Working with Apache Spark
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=========================
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============
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Apache Spark
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============
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.. admonition:: DataFlow

‎docs/source/user_guide/cli/opctl/localdev/vscode.rst‎

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**Prerequisites**
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1. ADS CLI is :doc:`configured<configure>`
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1. ADS CLI is :doc:`configured<../configure>`
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2. Install Visual Studio Code
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3. :doc:`Build Development Container Image<jobs_container_image>`
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4. Install Visual Studio Code extension for `Remote Development <https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.vscode-remote-extensionpack>`_

‎docs/source/user_guide/data_labeling/index.rst‎

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Labeling Data
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##########
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Label Data
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The Oracle Cloud Infrastructure (OCI) Data Labeling service allows you to create and browse datasets, view data records (text, images) and apply labels for the purposes of building AI/machine learning (ML) models. The service also provides interactive user interfaces that enable the labeling process. After you label records, you can export the dataset as line-delimited JSON Lines (JSONL) for use in model development.
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‎docs/source/user_guide/data_transformation/data_transformation.rst‎

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Data Transformations
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Transform Data
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When datasets are loaded with DatasetFactory, they can be transformed and manipulated easily with the built-in functions. Underlying, an ``ADSDataset`` object is a Pandas dataframe. Any operation that can be performed to a `Pandas dataframe <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_ can also be applied to an ADS Dataset.
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ds = DatasetFactory.open("path/data.csv").set_target('target')
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train, test = ds.train_test_split(test_size=0.25)
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Text Data
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*********
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../ADSString/index
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../text_extraction/text_dataset

‎docs/source/user_guide/data_visualization/visualization.rst‎

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Data Visualization
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Visualize Data
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Data visualization is an important aspect of data exploration, analysis, and communication. Generally, visualization of the data is one of the first steps in any analysis. It allows the analysts to efficiently gain an understanding of the data and guides the exploratory data analysis (EDA) and the modeling process.
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‎docs/source/user_guide/loading_data/connect.rst‎

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Loading Data
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#########
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Load Data
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Connecting to Data Sources

‎docs/source/user_guide/model_registration/introduction.rst‎

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Model Registration and Deployment
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Register and Deploy Models
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You could register your model with OCI Data Science service through ADS. Alternatively, the Oracle Cloud Infrastructure (OCI) Console can be used by going to the Data Science projects page, selecting a project, then click **Models**. The models page shows the model artifacts that are in the model catalog for a given project.

‎docs/source/user_guide/model_training/distributed_training/_profiling.rst‎

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**Profiling using Nvidia Nsights**
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`Nvidia Nsights <https://github.com/horovod/horovod/tree/master/examples/elastic/pytorch>`__. is a system wide profiling tool from Nvidia that can be used to profile Deep Learning workloads.
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Nsights requires no change in your training code. This works on process level. You can enable this experimental feature(highlighted in bold) in your training setup via the following configuration in the runtime yaml file.
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.. code-block:: bash
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- name: PROFILE
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value: 1
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- name: PROFILE_CMD
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value: ""nsys profile -w true -t cuda,nvtx,osrt,cudnn,cublas -s none -o /opt/ml/nsight_report -x true""
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Refer `this <https://docs.nvidia.com/nsight-systems/UserGuide/index.html#cli-profile-command-switch-options>`__ for nsys profile command options. You can modify the command within the ``PROFILE_CMD`` but remember this is all experimental. The profiling reports are generated per node. You need to download the reports to your computer manually or via the oci command.
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.. code-block:: bash
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oci os object bulk-download \
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-ns <namespace> \
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-bn <bucket_name> \
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--download-dir /path/on/your/computer \
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--prefix path/on/bucket/<job_id>
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To view the reports, you would need to install Nsight Systems app from `here <https://developer.nvidia.com/nsight-systems>`_. Thereafter, open the downloaded reports in the Nsight Systems app.

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