ignite_dataset {tfio}

Create a IgniteDataset.

Description

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale. This contrib package contains an integration between Apache Ignite and TensorFlow. The integration is based on tf.data from TensorFlow side and Binary Client Protocol from Apache Ignite side. It allows to use Apache Ignite as a datasource for neural network training, inference and all other computations supported by TensorFlow. Ignite Dataset is based on Apache Ignite Binary Client Protocol.

Usage

ignite_dataset(
  cache_name,
  host = "localhost",
  port = 10800,
  local = FALSE,
  part = -1,
  page_size = 100,
  username = NULL,
  password = NULL,
  certfile = NULL,
  keyfile = NULL,
  cert_password = NULL
)

Arguments

cache_name

Cache name to be used as datasource.

host

Apache Ignite Thin Client host to be connected.

port

Apache Ignite Thin Client port to be connected.

local

Local flag that defines to query only local data.

part

Number of partitions to be queried.

page_size

Apache Ignite Thin Client page size.

username

Apache Ignite Thin Client authentication username.

password

Apache Ignite Thin Client authentication password.

certfile

File in PEM format containing the certificate as well as any number of CA certificates needed to establish the certificate's authenticity.

keyfile

File containing the private key (otherwise the private key will be taken from certfile as well).

cert_password

Password to be used if the private key is encrypted and a password is necessary.

Examples

## Not run: 
dataset <- ignite_dataset(
    cache_name = "SQL_PUBLIC_TEST_CACHE", port = 10800) %>%
  dataset_repeat(1)

sess <- tf$Session()
iterator <- make_iterator_one_shot(dataset)
next_batch <- iterator_get_next(iterator)

until_out_of_range({
  batch <- sess$run(next_batch)
  print(batch)
})

## End(Not run)