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docs/en/docs/deployment/docker.md
### Number of Processes on the Official Docker Image The **number of processes** on this image is **computed automatically** from the CPU **cores** available. This means that it will try to **squeeze** as much **performance** from the CPU as possible. You can also adjust it with the configurations using **environment variables**, etc.
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Sat May 18 23:43:13 UTC 2024 - 34K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize.mlir
%0 = "tfl.squeeze"(%arg0) {squeeze_dims = [0]}: (tensor<*xf32>) -> tensor<*xf32> func.return %0: tensor<*xf32> // CHECK-LABEL: DontConvertSqueezeToReshape // CHECK: %[[RESULT:.*]] = "tfl.squeeze"(%arg0) // CHECK: return %[[RESULT]] } func.func @ConvertSqueezeToReshapeOnMultiDynamicDims(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 284.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/legalize_hlo.mlir
// CHECK-DAG: %[[CST_1:.*]] = "tf.Const"() <{value = dense<[1, 1504]> : tensor<2xi32>}> : () -> tensor<2xi32> // CHECK: %[[VAL_2:.*]] = "tf.Squeeze"(%[[VAL_1]]) <{squeeze_dims = [0]}> : (tensor<1x2xi32>) -> tensor<2xi32> // CHECK: %[[VAL_3:.*]] = "tf.Slice"(%[[ARG_0]], %[[VAL_2]], %[[CST_1]]) : (tensor<1x2944xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<1x1504xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 340.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc
if (!input_type) return success(); // Can't verify squeeze dims. int64_t input_rank = input_type.getRank(); for (const auto &squeeze_dim_apint : op.getSqueezeDims().getAsValueRange<IntegerAttr>()) { int64_t squeeze_dim = squeeze_dim_apint.getSExtValue(); if (squeeze_dim < -input_rank || squeeze_dim >= input_rank) { return op.emitOpError() << "squeeze dimension " << squeeze_dim << " not in ["
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 22:07:10 UTC 2024 - 170.8K bytes - Viewed (0) -
tensorflow/compiler/jit/mark_for_compilation_pass.cc
{"MISC", // clang-format off {"ApproxTopK", "BroadcastTo", "ExpandDims", "Fill", "NoOp", "Range", "Rank", "Reshape", "Shape", "ShapeN", "Size", "Squeeze", "Transpose", "ZerosLike", "OnesLike", "BiasAdd" /*PW + Broadcast*/, "BroadcastArgs", "BroadcastGradientArgs", "OneHot", "Concat", "ConcatV2",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 12:19:41 UTC 2024 - 85.3K bytes - Viewed (0) -
src/vendor/golang.org/x/crypto/sha3/sha3.go
for len(out) > 0 { n := copy(out, d.storage[d.i:d.n]) d.i += n out = out[n:] // Apply the permutation if we've squeezed the sponge dry. if d.i == d.rate { d.permute() } } return } // Sum applies padding to the hash state and then squeezes out the desired // number of output bytes. It panics if any output has already been read. func (d *state) Sum(in []byte) []byte {
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/mlir/tensorflow/tests/tf-ops.mlir
func.return } // ----- func.func @testSqueezeOutOfBounds(%arg0: tensor<?x?x10xf32>) -> tensor<?x10xf32> { // expected-error @+1 {{squeeze dimension -4 not in [-3, 3)}} %0 = "tf.Squeeze"(%arg0) { squeeze_dims = [-4] }: (tensor<?x?x10xf32>) -> tensor<?x10xf32> func.return %0 : tensor<?x10xf32> } // ----- func.func @testTernaryEinsum(%arg0: tensor<2x3xf32>){
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 23 14:40:35 UTC 2023 - 236.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.cc
// TFL_TransposeOp when the tensor has some dimensions with value==1 // Example- "tfl.transpose"(tensor<56x8x56x1x1x1x7xf32>, [4, 5, 1, 2, 0, 6, 3]) // Permutation before squeese is [4, 5, 1, 2, 0, 6, 3] becomes [1, 2, 0, 3] // after squeeze is perfomed to retain the relative ordering of the non-1 dims. DenseElementsAttr GetSqueezedPermutation(Value input_value, Value input_permutation) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 169.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf.cc
SmallVector<int64_t, 4> begin_indices(value_rank, 0); auto end_indices = llvm::to_vector<4>(value_type.getShape()); SmallVector<int64_t, 4> strides(value_rank, 1); // All HLO slice+squeeze results used to replace the original tf.Unpack op. SmallVector<Value, 4> results; results.reserve(op.getNumResults()); for (int i = 0, end = op.getNumResults(); i < end; ++i) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_canonicalize.td
include "mlir/IR/PatternBase.td" include "mlir/Dialect/Arith/IR/ArithOps.td" include "tensorflow/compiler/mlir/lite/ir/tfl_ops.td" include "tensorflow/compiler/mlir/lite/utils/utils.td" // Returns Squeezed shape of a ranked-tensor. // Squeezed, here, means eliminating any 1s' in the // dimensions of the tensor def GetSqueezedShape: NativeCodeCall<"GetSqueezedShape($0)">;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 13 20:41:03 UTC 2023 - 2.7K bytes - Viewed (0)