org.tensorflow.framework.CallableOptionsOrBuilder Maven / Gradle / Ivy
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/protobuf/config.proto
package org.tensorflow.framework;
public interface CallableOptionsOrBuilder extends
// @@protoc_insertion_point(interface_extends:tensorflow.CallableOptions)
com.google.protobuf.MessageOrBuilder {
/**
*
* Tensors to be fed in the callable. Each feed is the name of a tensor.
*
*
* repeated string feed = 1;
*/
java.util.List
getFeedList();
/**
*
* Tensors to be fed in the callable. Each feed is the name of a tensor.
*
*
* repeated string feed = 1;
*/
int getFeedCount();
/**
*
* Tensors to be fed in the callable. Each feed is the name of a tensor.
*
*
* repeated string feed = 1;
*/
java.lang.String getFeed(int index);
/**
*
* Tensors to be fed in the callable. Each feed is the name of a tensor.
*
*
* repeated string feed = 1;
*/
com.google.protobuf.ByteString
getFeedBytes(int index);
/**
*
* Fetches. A list of tensor names. The caller of the callable expects a
* tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
* order of specified fetches does not change the execution order.
*
*
* repeated string fetch = 2;
*/
java.util.List
getFetchList();
/**
*
* Fetches. A list of tensor names. The caller of the callable expects a
* tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
* order of specified fetches does not change the execution order.
*
*
* repeated string fetch = 2;
*/
int getFetchCount();
/**
*
* Fetches. A list of tensor names. The caller of the callable expects a
* tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
* order of specified fetches does not change the execution order.
*
*
* repeated string fetch = 2;
*/
java.lang.String getFetch(int index);
/**
*
* Fetches. A list of tensor names. The caller of the callable expects a
* tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
* order of specified fetches does not change the execution order.
*
*
* repeated string fetch = 2;
*/
com.google.protobuf.ByteString
getFetchBytes(int index);
/**
*
* Target Nodes. A list of node names. The named nodes will be run by the
* callable but their outputs will not be returned.
*
*
* repeated string target = 3;
*/
java.util.List
getTargetList();
/**
*
* Target Nodes. A list of node names. The named nodes will be run by the
* callable but their outputs will not be returned.
*
*
* repeated string target = 3;
*/
int getTargetCount();
/**
*
* Target Nodes. A list of node names. The named nodes will be run by the
* callable but their outputs will not be returned.
*
*
* repeated string target = 3;
*/
java.lang.String getTarget(int index);
/**
*
* Target Nodes. A list of node names. The named nodes will be run by the
* callable but their outputs will not be returned.
*
*
* repeated string target = 3;
*/
com.google.protobuf.ByteString
getTargetBytes(int index);
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
boolean hasRunOptions();
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
org.tensorflow.framework.RunOptions getRunOptions();
/**
*
* Options that will be applied to each run.
*
*
* .tensorflow.RunOptions run_options = 4;
*/
org.tensorflow.framework.RunOptionsOrBuilder getRunOptionsOrBuilder();
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
java.util.List
getTensorConnectionList();
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
org.tensorflow.framework.TensorConnection getTensorConnection(int index);
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
int getTensorConnectionCount();
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
java.util.List
getTensorConnectionOrBuilderList();
/**
*
* Tensors to be connected in the callable. Each TensorConnection denotes
* a pair of tensors in the graph, between which an edge will be created
* in the callable.
*
*
* repeated .tensorflow.TensorConnection tensor_connection = 5;
*/
org.tensorflow.framework.TensorConnectionOrBuilder getTensorConnectionOrBuilder(
int index);
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
int getFeedDevicesCount();
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
boolean containsFeedDevices(
java.lang.String key);
/**
* Use {@link #getFeedDevicesMap()} instead.
*/
@java.lang.Deprecated
java.util.Map
getFeedDevices();
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
java.util.Map
getFeedDevicesMap();
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
java.lang.String getFeedDevicesOrDefault(
java.lang.String key,
java.lang.String defaultValue);
/**
*
* The Tensor objects fed in the callable and fetched from the callable
* are expected to be backed by host (CPU) memory by default.
* The options below allow changing that - feeding tensors backed by
* device memory, or returning tensors that are backed by device memory.
* The maps below map the name of a feed/fetch tensor (which appears in
* 'feed' or 'fetch' fields above), to the fully qualified name of the device
* owning the memory backing the contents of the tensor.
* For example, creating a callable with the following options:
* CallableOptions {
* feed: "a:0"
* feed: "b:0"
* fetch: "x:0"
* fetch: "y:0"
* feed_devices: {
* "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* fetch_devices: {
* "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
* }
* }
* means that the Callable expects:
* - The first argument ("a:0") is a Tensor backed by GPU memory.
* - The second argument ("b:0") is a Tensor backed by host memory.
* and of its return values:
* - The first output ("x:0") will be backed by host memory.
* - The second output ("y:0") will be backed by GPU memory.
* FEEDS:
* It is the responsibility of the caller to ensure that the memory of the fed
* tensors will be correctly initialized and synchronized before it is
* accessed by operations executed during the call to Session::RunCallable().
* This is typically ensured by using the TensorFlow memory allocators
* (Device::GetAllocator()) to create the Tensor to be fed.
* Alternatively, for CUDA-enabled GPU devices, this typically means that the
* operation that produced the contents of the tensor has completed, i.e., the
* CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
* cuStreamSynchronize()).
*
*
* map<string, string> feed_devices = 6;
*/
java.lang.String getFeedDevicesOrThrow(
java.lang.String key);
/**
* map<string, string> fetch_devices = 7;
*/
int getFetchDevicesCount();
/**
* map<string, string> fetch_devices = 7;
*/
boolean containsFetchDevices(
java.lang.String key);
/**
* Use {@link #getFetchDevicesMap()} instead.
*/
@java.lang.Deprecated
java.util.Map
getFetchDevices();
/**
* map<string, string> fetch_devices = 7;
*/
java.util.Map
getFetchDevicesMap();
/**
* map<string, string> fetch_devices = 7;
*/
java.lang.String getFetchDevicesOrDefault(
java.lang.String key,
java.lang.String defaultValue);
/**
* map<string, string> fetch_devices = 7;
*/
java.lang.String getFetchDevicesOrThrow(
java.lang.String key);
/**
*
* By default, RunCallable() will synchronize the GPU stream before returning
* fetched tensors on a GPU device, to ensure that the values in those tensors
* have been produced. This simplifies interacting with the tensors, but
* potentially incurs a performance hit.
* If this options is set to true, the caller is responsible for ensuring
* that the values in the fetched tensors have been produced before they are
* used. The caller can do this by invoking `Device::Sync()` on the underlying
* device(s), or by feeding the tensors back to the same Session using
* `feed_devices` with the same corresponding device name.
*
*
* bool fetch_skip_sync = 8;
*/
boolean getFetchSkipSync();
}
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