LostTech.TensorFlow : API Documentation

An asynchronous multi-worker parameter server tf.distribute strategy.

This strategy requires two jobs: workers and parameter servers. Variables and updates to those variables will be assigned to parameter servers and other operations are assigned to workers.

When each worker has more than one GPU, operations will be replicated on all GPUs. Even though operations may be replicated, variables are not and each worker shares a common view for which parameter server a variable is assigned to.

By default it uses `TFConfigClusterResolver` to detect configurations for multi-worker training. This requires a 'TF_CONFIG' environment variable and the 'TF_CONFIG' must have a cluster spec.

This class assumes each worker is running the same code independently, but parameter servers are running a standard server. This means that while each worker will synchronously compute a single gradient update across all GPUs, updates between workers proceed asynchronously. Operations that occur only on the first replica (such as incrementing the global step), will occur on the first replica *of every worker*.

It is expected to call `call_for_each_replica(fn,...)` for any operations which potentially can be replicated across replicas (i.e. multiple GPUs) even if there is only CPU or one GPU. When defining the `fn`, extra caution needs to be taken:

1) It is generally not recommended to open a device scope under the strategy's scope. A device scope (i.e. calling tf.device) will be merged with or override the device for operations but will not change the device for variables.

2) It is also not recommended to open a colocation scope (i.e. calling `tf.compat.v1.colocate_with`) under the strategy's scope. For colocating variables, use `strategy.extended.colocate_vars_with` instead. Colocation of ops will possibly create device assignment conflicts.

Note: This strategy only works with the Estimator API. Pass an instance of this strategy to the `experimental_distribute` argument when you create the `RunConfig`. This instance of `RunConfig` should then be passed to the `Estimator` instance on which `train_and_evaluate` is called.

For Example: ``` strategy = tf.distribute.experimental.ParameterServerStrategy() run_config = tf.estimator.RunConfig( experimental_distribute.train_distribute=strategy) estimator = tf.estimator.Estimator(config=run_config) tf.estimator.train_and_evaluate(estimator,...)