tabs (Onglets)¶

Introduction¶

Tabs organize and allow navigation between groups of content that are related and at the same level of hierarchy.

Each tab should contain content that is distinct from other tabs in a set.

See the Material Design description for further details.

Example from ray code (1)¶

https://docs.ray.io/en/latest/ray-core/actors.html#ray-remote-classes

.. tab-set-code::

    .. literalinclude:: src/ray.py
       :language: python

    .. literalinclude:: src/ray.java
       :language: java

    .. literalinclude:: src/ray.c++
       :language: c++

PYTHON

@ray.remote
class Counter(object):
    def __init__(self):
        self.value = 0

    def increment(self):
        self.value += 1
        return self.value

    def get_counter(self):
        return self.value


# Create an actor from this class.
counter = Counter.remote()

JAVA

// A regular Java class.
public class Counter {

  private int value = 0;

  public int increment() {
    this.value += 1;
    return this.value;
  }
}

// Create an actor from this class.
// `Ray.actor` takes a factory method that can produce
// a `Counter` object. Here, we pass `Counter`'s constructor
// as the argument.
ActorHandle<Counter> counter = Ray.actor(Counter::new).remote();

C++

// A regular C++ class.
class Counter {

private:
    int value = 0;

public:
  int Increment() {
    value += 1;
    return value;
  }
};

// Factory function of Counter class.
static Counter *CreateCounter() {
    return new Counter();
};

RAY_REMOTE(&Counter::Increment, CreateCounter);

// Create an actor from this class.
// `ray::Actor` takes a factory method that can produce
// a `Counter` object. Here, we pass `Counter`'s factory function
// as the argument.
auto counter = ray::Actor(CreateCounter).Remote();

Example from ray code (2)¶

.. tab-set::

    .. tab-item:: NumPy (default)

      The ``"numpy"`` option presents batches as ``Dict[str, np.ndarray]``, where the
      `numpy.ndarray <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html>`__
      values represent a batch of record field values.

      .. literalinclude:: ./doc_code/transforming_data.py
        :language: python
        :start-after: __writing_numpy_udfs_begin__
        :end-before: __writing_numpy_udfs_end__

    .. tab-item:: Pandas

      The ``"pandas"`` batch format presents batches in
      `pandas.DataFrame <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`__
      format.

      .. literalinclude:: ./doc_code/transforming_data.py
        :language: python
        :start-after: __writing_pandas_udfs_begin__
        :end-before: __writing_pandas_udfs_end__

    .. tab-item:: PyArrow

      The ``"pyarrow"`` batch format presents batches in
      `pyarrow.Table <https://arrow.apache.org/docs/python/generated/pyarrow.Table.html>`__
      format.

      .. literalinclude:: ./doc_code/transforming_data.py
        :language: python
        :start-after: __writing_arrow_udfs_begin__
        :end-before: __writing_arrow_udfs_end__

NumPy (default)

The "numpy" option presents batches as Dict[str, np.ndarray], where the numpy.ndarray values represent a batch of record field values.

import ray
import numpy as np
from typing import Dict

ds = ray.data.read_csv("example://iris.csv")

def numpy_transform(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
    new_col = batch["sepal.length"] / np.max(batch["sepal.length"])
    batch["normalized.sepal.length"] = new_col
    del batch["sepal.length"]
    return batch

ds.map_batches(numpy_transform, batch_format="numpy").show(2)
# -> {'sepal.width': 3.2, 'petal.length': 4.7, 'petal.width': 1.4,
#     'variety': 'Versicolor', 'normalized.sepal.length': 1.0}
# -> {'sepal.width': 3.2, 'petal.length': 4.5, 'petal.width': 1.5,
#     'variety': 'Versicolor', 'normalized.sepal.length': 0.9142857142857144}

Pandas

The "pandas" batch format presents batches in pandas.DataFrame format.

import ray
import pandas as pd

ds = ray.data.read_csv("example://iris.csv")

def pandas_transform(df: pd.DataFrame) -> pd.DataFrame:
    df.loc[:, "normalized.sepal.length"] = df["sepal.length"] / df["sepal.length"].max()
    df = df.drop(columns=["sepal.length"])
    return df

ds.map_batches(pandas_transform, batch_format="pandas").show(2)
# -> {'sepal.width': 3.2, 'petal.length': 4.7, 'petal.width': 1.4,
#     'variety': 'Versicolor', 'normalized.sepal.length': 1.0}
# -> {'sepal.width': 3.2, 'petal.length': 4.5, 'petal.width': 1.5,
#     'variety': 'Versicolor', 'normalized.sepal.length': 0.9142857142857144}

PyArrow

The "pyarrow" batch format presents batches in pyarrow.Table format.

import ray
import pyarrow as pa
import pyarrow.compute as pac

ds = ray.data.read_csv("example://iris.csv")

def pyarrow_transform(batch: pa.Table) -> pa.Table:
    batch = batch.append_column(
        "normalized.sepal.length",
        pac.divide(batch["sepal.length"], pac.max(batch["sepal.length"])),
    )
    return batch.drop(["sepal.length"])

ds.map_batches(pyarrow_transform, batch_format="pyarrow").show(2)
# -> {'sepal.width': 3.2, 'petal.length': 4.7, 'petal.width': 1.4,
#     'variety': 'Versicolor', 'normalized.sepal.length': 1.0}
# -> {'sepal.width': 3.2, 'petal.length': 4.5, 'petal.width': 1.5,
#     'variety': 'Versicolor', 'normalized.sepal.length': 0.9142857142857144}

Synchronised Tabs¶

Use the sync option to synchronise the selected tab items across multiple tab-sets.

Note, synchronisation requires that JavaScript is enabled.

Label1

Content 1

Label2

Content 2

Label1

Content 1

Label2

Content 2

tabs (Onglets)¶

Introduction¶

Example from ray code (1)¶

Example from ray code (2)¶

Synchronised Tabs¶

Example from ray code ray-cluster-configuration¶

`auth.ssh_private_key`¶

`auth.ssh_public_key`¶

Minimal configuration¶

TPU Configuration¶

tabs (Onglets)¶

Introduction¶

Example from ray code (1)¶

Example from ray code (2)¶

Synchronised Tabs¶

Example from ray code ray-cluster-configuration¶

auth.ssh_private_key¶

auth.ssh_public_key¶

Minimal configuration¶

TPU Configuration¶

`auth.ssh_private_key`¶

`auth.ssh_public_key`¶