*
 * Mappings Data (4,719 bytes)
 * ===========================
 *
 * This is UTF-8 character data. It is indexed into by b2b3 in the ranges dataset.
 *
 * Mappings may overlap.
 *
 * ASCII-Only
 * ==========
 *
 * Neither the section index nor the ranges data use bit 0x80 anywhere. That means the data is

 *  Fix: Recover gracefully when a cache entry's certificate is corrupted.

 *  Fix: Fail permanently when there's a failure loading the bundled public suffix database.
    This is the dataset that powers `HttpUrl.topPrivateDomain()`.

 *  Fix: Immediately update the connection's flow control window instead of waiting for the
    receiving stream to process it.

message FlockerVolumeSource {
  // datasetName is Name of the dataset stored as metadata -> name on the dataset for Flocker
  // should be considered as deprecated
  // +optional
  optional string datasetName = 1;

  // datasetUUID is the UUID of the dataset. This is unique identifier of a Flocker dataset
  // +optional
  optional string datasetUUID = 2;
}

*   `tf.data`

    *   `tf.data.Dataset.zip` now supports Python-style zipping, i.e. `Dataset.zip(a, b, c)`.
    * `tf.data.Dataset.shuffle` now supports `tf.data.UNKNOWN_CARDINALITY` When doing a "full shuffle" using  `dataset = dataset.shuffle(dataset.cardinality())`. But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).

#### Option A: Reference colab notebooks

1)  Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.

MinIO's Object Lambda implementation allows for transforming your data to serve unique data format requirements for each application. For example, a dataset created by an ecommerce application might include personally identifiable information (PII). When the same data is processed for analytics, PII should be redacted. However, if the same dataset is used for a marketing campaign, you might need to enrich the data with additional details, such as information from the customer loyalty database.

        print(statsDetails['BytesScanned'])
        print("Stats details bytesProcessed: ")
        print(statsDetails['BytesProcessed'])
```

## 4. Run the Program

Upload a sample dataset to MinIO using the following commands.

```sh
curl "https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_TotalPopulationBySex.csv" > TotalPopulation.csv
mc mb myminio/mycsvbucket

        "//tensorflow/core/grappler:grappler_item",
        "//tensorflow/core/grappler:grappler_item_builder",
        "//tensorflow/core/kernels:data_service_ops",
        "//tensorflow/core/kernels:dataset_ops",
        "//tensorflow/core/platform:logging",
        "//tensorflow/core/platform:path",
        "//tensorflow/core/platform:stacktrace_handler",
        "//tensorflow/core/platform:statusor",

### **4.2 WordCount**

WordCount is a simple program that counts how often a word occurs in a text file. The code builds a dataset of (String, Int) pairs called counts, and saves the dataset to a file.

The following example submits WordCount code to the Scala shell. Select an input file for the Spark WordCount example. We can use any text file as input.

- Login as user **‘spark’**.