CallableOptionsOrBuilder

공개 인터페이스 CallableOptionsOrBuilder
알려진 간접 하위 클래스

공개 방법

추상 부울
containFeedDevices (문자열 키) 
 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
추상 부울
containFetchDevices (문자열 키)
map<string, string> fetch_devices = 7;
추상 문자열
getFeed (정수 인덱스) 
 Tensors to be fed in the callable.
추상 com.google.protobuf.ByteString
getFeedBytes (정수 인덱스) 
 Tensors to be fed in the callable.
추상 정수
getFeedCount () 
 Tensors to be fed in the callable.
추상 맵<문자열, 문자열>
getFeedDevices ()
대신 getFeedDevicesMap() 사용하세요.
추상 정수
getFeedDevicesCount () 
 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
추상 맵<문자열, 문자열>
getFeedDevicesMap () 
 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
추상 문자열
getFeedDevicesOrDefault (문자열 키, 문자열 defaultValue) 
 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
추상 문자열
getFeedDevicesOrThrow (문자열 키) 
 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
추상 목록<문자열>
getFeedList () 
 Tensors to be fed in the callable.
추상 문자열
getFetch (정수 인덱스) 
 Fetches.
추상 com.google.protobuf.ByteString
getFetchBytes (정수 인덱스) 
 Fetches.
추상 정수
getFetchCount () 
 Fetches.
추상 맵<문자열, 문자열>
getFetchDevices ()
대신 getFetchDevicesMap() 사용하세요.
추상 정수
getFetchDevicesCount ()
map<string, string> fetch_devices = 7;
추상 맵<문자열, 문자열>
getFetchDevicesMap ()
map<string, string> fetch_devices = 7;
추상 문자열
getFetchDevicesOrDefault (문자열 키, 문자열 defaultValue)
map<string, string> fetch_devices = 7;
추상 문자열
getFetchDevicesOrThrow (문자열 키)
map<string, string> fetch_devices = 7;
추상 목록<문자열>
getFetchList () 
 Fetches.
추상 부울
getFetchSkipSync () 
 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.
추상 실행 옵션
getRunOptions () 
 Options that will be applied to each run.
추상 RunOptionsOrBuilder
getRunOptionsOrBuilder () 
 Options that will be applied to each run.
추상 문자열
getTarget (정수 인덱스) 
 Target Nodes.
추상 com.google.protobuf.ByteString
getTargetBytes (정수 인덱스) 
 Target Nodes.
추상 정수
getTargetCount () 
 Target Nodes.
추상 목록<문자열>
getTargetList () 
 Target Nodes.
추상 TensorConnection
getTensorConnection (정수 인덱스) 
 Tensors to be connected in the callable.
추상 정수
getTensorConnectionCount () 
 Tensors to be connected in the callable.
추상 목록< TensorConnection >
getTensorConnectionList () 
 Tensors to be connected in the callable.
추상 TensorConnectionOrBuilder
getTensorConnectionOrBuilder (정수 인덱스) 
 Tensors to be connected in the callable.
추상 목록<? TensorConnectionOrBuilder 확장 >
getTensorConnectionOrBuilderList () 
 Tensors to be connected in the callable.
추상 부울
hasRunOptions () 
 Options that will be applied to each run.

공개 방법

공개 추상 부울 containFeedDevices (문자열 키) 

 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;

공개 추상 부울 containFetchDevices (문자열 키)

map<string, string> fetch_devices = 7;

공개 추상 문자열 getFeed (int 인덱스) 

 Tensors to be fed in the callable. Each feed is the name of a tensor.
repeated string feed = 1;

공개 추상 com.google.protobuf.ByteString getFeedBytes (int 인덱스) 

 Tensors to be fed in the callable. Each feed is the name of a tensor.
repeated string feed = 1;

공개 추상 int getFeedCount () 

 Tensors to be fed in the callable. Each feed is the name of a tensor.
repeated string feed = 1;

공개 추상 Map<String, String> getFeedDevices ()

대신 getFeedDevicesMap() 사용하세요.

공개 추상 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;

공개 추상 Map<String, String> 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;

공개 추상 문자열 getFeedDevicesOrDefault (문자열 키, 문자열 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;

공개 추상 String getFeedDevicesOrThrow (문자열 키) 

 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;

공개 추상 List<String> getFeedList () 

 Tensors to be fed in the callable. Each feed is the name of a tensor.
repeated string feed = 1;

공개 추상 문자열 getFetch (int 인덱스) 

 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;

공개 추상 com.google.protobuf.ByteString getFetchBytes (int 인덱스) 

 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;

공개 추상 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;

공개 추상 Map<String, String> getFetchDevices ()

대신 getFetchDevicesMap() 사용하세요.

공개 추상 int getFetchDevicesCount ()

map<string, string> fetch_devices = 7;

공개 추상 Map<String, String> getFetchDevicesMap ()

map<string, string> fetch_devices = 7;

공개 추상 문자열 getFetchDevicesOrDefault (문자열 키, 문자열 defaultValue)

map<string, string> fetch_devices = 7;

공개 추상 String getFetchDevicesOrThrow (문자열 키)

map<string, string> fetch_devices = 7;

공개 추상 List<String> 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;

공개 추상 부울 getFetchSkipSync () 

 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;

공개 추상 RunOptions getRunOptions () 

 Options that will be applied to each run.
.tensorflow.RunOptions run_options = 4;

공개 추상 RunOptionsOrBuilder getRunOptionsOrBuilder () 

 Options that will be applied to each run.
.tensorflow.RunOptions run_options = 4;

공개 추상 문자열 getTarget (int 인덱스) 

 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;

공개 추상 com.google.protobuf.ByteString getTargetBytes (int 인덱스) 

 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;

공개 추상 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;

공개 추상 List<String> 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;

공개 추상 TensorConnection getTensorConnection (int 인덱스) 

 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;

공개 추상 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;

공개 추상 목록< TensorConnection > 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;

공개 추상 TensorConnectionOrBuilder getTensorConnectionOrBuilder (int 인덱스) 

 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;

공개 요약 목록<? TensorConnectionOrBuilder > getTensorConnectionOrBuilderList () 를 확장합니다. 

 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;

공개 추상 부울 hasRunOptions () 

 Options that will be applied to each run.
.tensorflow.RunOptions run_options = 4;