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pkg/lazy/lazy_test.go
"istio.io/istio/pkg/slices" "istio.io/istio/pkg/test/util/assert" ) func TestLazySerial(t *testing.T) { t.Run("retry", func(t *testing.T) { computations := atomic.NewInt32(0) l := NewWithRetry(func() (int32, error) { res := computations.Inc() if res > 2 { return res, nil } return res, fmt.Errorf("not yet") }) res, err := l.Get() assert.Error(t, err)
Registered: Fri Jun 14 15:00:06 UTC 2024 - Last Modified: Wed May 24 17:36:41 UTC 2023 - 3K bytes - Viewed (0) -
tensorflow/compiler/jit/pjrt_base_device.h
// a) argument and return value, for entry computations b) variables, for // all computations, should be represented in XLA. Parameters/return values // will be shaped according to the function pair, and reshaped back to/from // their declared shapes for computations. Must be non-empty. std::vector<XlaShapeLayoutHelpers::ShapeDeterminationFns> shape_determination_fns;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 12:19:41 UTC 2024 - 4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_device.h
limitations under the License. ==============================================================================*/ // This file defines the tf_device dialect: it contains operations that model // TensorFlow's actions to launch computations on accelerator devices. #ifndef TENSORFLOW_COMPILER_MLIR_TENSORFLOW_IR_TF_DEVICE_H_ #define TENSORFLOW_COMPILER_MLIR_TENSORFLOW_IR_TF_DEVICE_H_ #include "mlir/Dialect/Func/IR/FuncOps.h" // from @llvm-project
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 22 14:25:57 UTC 2022 - 2.2K bytes - Viewed (0) -
tensorflow/compiler/jit/encapsulate_xla_computations_pass.h
// Rewrites computations generated by the xla.compile() Python code into // XlaLaunch nodes. // // xla.compile() does two main things: // a) marks operators that make up an XLA computation with the attribute // _xla_compile_id=XYZ, where XYZ is a unique key. // b) adds XlaClusterOutput nodes to represent outputs of the computation. // These nodes are not marked with the _xla_compile_id attribute.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 06:59:07 UTC 2024 - 3.6K bytes - Viewed (0) -
platforms/core-execution/persistent-cache/src/test/groovy/org/gradle/cache/ManualEvictionInMemoryCacheTest.groovy
import java.util.function.Supplier class ManualEvictionInMemoryCacheTest extends Specification { @Timeout(value = 5, unit = TimeUnit.SECONDS) def "supports #concurrency concurrent computations"() { def latch = new CountDownLatch(concurrency) def executor = Executors.newFixedThreadPool(concurrency) def cache = new ManualEvictionInMemoryCache<String, String>() when:
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Fri Sep 22 09:08:47 UTC 2023 - 1.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/decompose_resource_ops.h
namespace mlir { namespace TF { // Populates rewrite patterns that decompose composite resource operations into // primitive ones like ReadVariableOp, AssignVariableOp and other computations // to facilitate transformations like resource op lifting. // NOTE: These patterns do not support `use_locking=true` for a lot of resource // operations. So decomposition may not be correct outside of backends like XLA,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jan 27 15:05:02 UTC 2022 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/ops/tf_op_quant_spec.h
#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h" namespace mlir { namespace quant { // Check if the op has data movement trait. Ops with this trait do not perform // any computations but just move data and has one result operand. bool IsOpWithDataMovementTrait(Operation* op); // Check if the op is quantizable. Currently, the scope of quantizable op is
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 05 07:39:40 UTC 2024 - 2.5K bytes - Viewed (0) -
src/vendor/golang.org/x/crypto/sha3/shake.go
// a customizable variant of SHAKE128. // N is used to define functions based on cSHAKE, it can be empty when plain cSHAKE is // desired. S is a customization byte string used for domain separation - two cSHAKE // computations on same input with different S yield unrelated outputs. // When N and S are both empty, this is equivalent to NewShake128. func NewCShake128(N, S []byte) ShakeHash { if len(N) == 0 && len(S) == 0 { return NewShake128()
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Tue Jun 04 16:19:04 UTC 2024 - 5.4K bytes - Viewed (0) -
tensorflow/compiler/jit/jit_compilation_pass_registration.cc
#include "tensorflow/core/common_runtime/optimization_registry.h" namespace tensorflow { // PRE_PLACEMENT passes: // EncapsulateXlaComputationsPass rewrites computations generated by the // xla.compile() Python code into XlaLaunch nodes. REGISTER_OPTIMIZATION(OptimizationPassRegistry::PRE_PLACEMENT, 36, EncapsulateXlaComputationsPass); // from
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jan 11 21:53:08 UTC 2023 - 3.8K bytes - Viewed (0) -
pkg/controller/podautoscaler/monitor/metrics.go
metricComputationTotal = metrics.NewCounterVec( &metrics.CounterOpts{ Subsystem: hpaControllerSubsystem, Name: "metric_computation_total", Help: "Number of metric computations. The label 'action' should be either 'scale_down', 'scale_up', or 'none'. Also, the label 'error' should be either 'spec', 'internal', or 'none'. The label 'metric_type' corresponds to HPA.spec.metrics[*].type",
Registered: Sat Jun 15 01:39:40 UTC 2024 - Last Modified: Tue Mar 14 22:47:24 UTC 2023 - 3.7K bytes - Viewed (0)