An optional, fourth parameter was added TfLiteOperatorCreate as a step forward towards a cleaner API for TfLiteOperator. Function TfLiteOperatorCreate was added recently, in TensorFlow Lite version 2.17.0, released on 7/11/2024, and we do not expect there will be much code using this function yet. Any code breakages can be easily resolved by passing nullptr as the new, 4th parameter.
TensorRT support is disabled in CUDA builds for code health improvement.
Hermetic CUDA support is added.
Hermetic CUDA uses a specific downloadable version of CUDA instead of the user’s locally installed CUDA. Bazel will download CUDA, CUDNN and NCCL distributions, and then use CUDA libraries and tools as dependencies in various Bazel targets. This enables more reproducible builds for Google ML projects and supported CUDA versions.
Note that NumPy's type promotion rules have been changed(See NEP 50for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results.
Tensorflow will continue to support NumPy 1.26 until 2025, aligning with community standard deprecation timeline here.
tf.lite:
The LiteRT repo is live (see announcement), which means that in the coming months there will be changes to the development experience for TFLite. The TF Lite Runtime source will be moved later this year, and sometime after that we will start accepting contributions through that repo.
SignatureRunner is now supported for models with no signatures.
Bug Fixes and Other Changes
tf.data
Add optional synchronous argument to map, to specify that the map should run synchronously, as opposed to be parallelizable when options.experimental_optimization.map_parallelization=True. This saves memory compared to setting num_parallel_calls=1.
Add optional use_unbounded_threadpool argument to map, to specify that the map should use an unbounded threadpool instead of the default pool that is based on the number of cores on the machine. This can improve throughput for map functions which perform IO or otherwise release the CPU.
An optional, fourth parameter was added TfLiteOperatorCreate as a step forward towards a cleaner API for TfLiteOperator. Function TfLiteOperatorCreate was added recently, in TensorFlow Lite version 2.17.0, released on 7/11/2024, and we do not expect there will be much code using this function yet. Any code breakages can be easily resolved by passing nullptr as the new, 4th parameter.
TensorRT support is disabled in CUDA builds for code health improvement.
Note that NumPy's type promotion rules have been changed(See NEP 50for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results.
Tensorflow will continue to support NumPy 1.26 until 2025, aligning with community standard deprecation timeline here.
Hermetic CUDA support is added.
Hermetic CUDA uses a specific downloadable version of CUDA instead of the user’s locally installed CUDA. Bazel will download CUDA, CUDNN and NCCL distributions, and then use CUDA libraries and tools as dependencies in various Bazel targets. This enables more reproducible builds for Google ML projects and supported CUDA versions.
Remove the EnumNamesXNNPackFlags function in tensorflow/lite/acceleration/configuration/configuration_generated.h.
The LiteRT repo is live (see announcement), which means that in the coming months there will be changes to the development experience for TFLite. The TF Lite Runtime source will be moved later this year, and sometime after that we will start accepting contributions through that repo.
SignatureRunner is now supported for models with no signatures.
Bug Fixes and Other Changes
tf.data
Add optional synchronous argument to map, to specify that the map should run synchronously, as opposed to be parallelizable when options.experimental_optimization.map_parallelization=True. This saves memory compared to setting num_parallel_calls=1.
Add optional use_unbounded_threadpool argument to map, to specify that the map should use an unbounded threadpool instead of the default pool that is based on the number of cores on the machine. This can improve throughput for map functions which perform IO or otherwise release the CPU.
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Bumps tensorflow from 2.11.0 to 2.18.0.
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Commits
6550e4bMerge pull request #78464 from tensorflow/rtg0795-patch-17e0c244Merge pull request #78463 from tensorflow-jenkins/version-numbers-2.18.0-2110135624d2Update RELEASE.md to move TFLite SignatureRunner to the right section8d2c5e1Update version numbers to 2.18.0d5f4a3fMerge pull request #77589 from tensorflow-jenkins/version-numbers-2.18.0rc2-1...7cbcbf3Update version numbers to 2.18.0-rc284c9398Merge pull request #77576 from tensorflow/r2.18-be4f646ec438fca5e7PR #17430: [ROCm] Use unique_ptr for TupleHandle in pjrt_se_client2c3c798Merge pull request #77025 from tensorflow-jenkins/version-numbers-2.18.0rc1-2...10693a4Update version numbers to 2.18.0-rc1Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase.Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show