[feat]Add Redis backend feature to TFRA

[MoFHeka]

Add Redis backend feature to TFRA

Description

Brief Description of the PR:

Added Redis as the backend for KV storage and changed the Python interface by calling the Config class and creating/passing the KvCreator class to fit the different backend. Also setting backend parameters with json file.

Remember the devices of TFRA-Redis should be set as local, not PS!

Type of change

• Bug fix
• New Tutorial
• Updated or additional documentation
• Additional Testing
• New Feature

Checklist:

• I've properly formatted my code according to the guidelines
  • By running yapf
  • By running clang-format
• This PR addresses an already submitted issue for TensorFlow Recommenders-Addons
• I have made corresponding changes to the documentation
• I have added tests that prove my fix is effective or that my feature works

How Has This Been Tested?

redis_config1=tfra.dynamic_embedding.RedisTableConfig(
    redis_config_abs_dir=“/tmp/test/redis_config.json”
)
redis_creator1=tfra.dynamic_embedding.RedisTableCreator(redis_config1)

self.user_embeddings = tfra.dynamic_embedding.get_variable(
            name="user_dynamic_embeddings",
            dim=self.embedding_size,
            initializer=tf.keras.initializers.RandomNormal(-1, 1),
            checkpoint=False,
            KVCreator=redis_creator1
            )

or you can do it like this:

redis_config_dir = os.path.join(tempfile.mkdtemp(dir=os.environ.get('TEST_TMPDIR')), "save_restore")
redis_config_path = os.path.join(tempfile.mkdtemp(prefix=redis_config_dir), "hash")
os.makedirs(redis_config_path)
redis_config_path = os.path.join(redis_config_path, "redis_config.json")
redis_config_params = {
  "redis_host_ip":["127.0.0.1"],
  "redis_host_port":[6379],
  "using_model_lib":False
}
with open(redis_config_path, 'w', encoding='utf-8') as f:
  f.write(json.dumps(redis_config_params, indent=2, ensure_ascii=True))
redis_config = de.RedisTableConfig(
  redis_config_abs_dir=redis_config_path
)
redis_creator=tfra.dynamic_embedding.RedisTableCreator(redis_config)

self.user_embeddings = tfra.dynamic_embedding.get_variable(
            name="user_dynamic_embeddings",
            dim=self.embedding_size,
            initializer=tf.keras.initializers.RandomNormal(-1, 1),
            checkpoint=False,
            KVCreator= redis_creator
            )

How Has This Been Used?

Please visit this folder:
demo/dynamic_embedding/movielens-100k-estimator-redis

[rhdong]

LGTM

[rhdong]

Great, thank you HeJia! @MoFHeka