Bump tensorflow from 2.15.0.post1 to 2.18.1

[dependabot]

Bumps tensorflow from 2.15.0.post1 to 2.18.1.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.18.1

Release 2.18.1

Security

• Updates curl to 8.11.0 to handle CVE-2024-2004, CVE-2024-2379, CVE-2024-2398, CVE-2024-2466, CVE-2024-6197, CVE-2024-7264, CVE-2024-8096 and CVE-2024-9681.

Bug Fixes and Other Changes

• Loosen ml_dtypes upperbound to < 1.0.0 to reduce conflicts when installed with other ML ecosystem components.

Breaking Changes

• tf.lite
  • Interpreter:
    • tf.lite.Interpreter gives warning of future deletion and a redirection notice to its new location at ai_edge_litert.interpreter. See the migration guide for details.
• Tensorflow-tpu for this patch is skipped due to some sparsecore related bugs. We suggest to upgrade to 2.19.0 instead.

TensorFlow 2.18.0

Release 2.18.0

TensorFlow

Breaking Changes

• tf.lite

  • C API:
    • 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.

Known Caveats

Major Features and Improvements

• TensorFlow now supports and is compiled with NumPy 2.0 by default. Please see the NumPy 2 release notes and the NumPy 2 migration guide.
  • 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.

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.18.1

Security

• Updates curl to 8.11.0 to handle CVE-2024-2004, CVE-2024-2379, CVE-2024-2398, CVE-2024-2466, CVE-2024-6197, CVE-2024-7264, CVE-2024-8096 and CVE-2024-9681.

Bug Fixes and Other Changes

• Loosen ml_dtypes upperbound to < 1.0.0 to reduce conflicts when installed with other ML ecosystem components.

Breaking Changes

• tf.lite
  • Interpreter:
    • tf.lite.Interpreter gives warning of future deletion and a redirection notice to its new location at ai_edge_litert.interpreter. See the migration guide for details.
• Tensorflow-tpu for this patch is skipped due to some sparsecore related bugs. We suggest to upgrade to 2.19.0 instead.

Release 2.18.0

TensorFlow

Breaking Changes

• tf.lite

  • Interpreter:
    • tf.lite.Interpreter gives warning of future deletion and a redirection notice to its new location at ai_edge_litert.interpreter. See the migration guide for details.
  • C API:
    • 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.

Known Caveats

Major Features and Improvements

• TensorFlow now supports and is compiled with NumPy 2.0 by default. Please see the NumPy 2 release notes and the NumPy 2 migration guide.
  • 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.

... (truncated)

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