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tensorflow-lite-devel-2.10.0-3.1 RPM for riscv64

From OpenSuSE Ports Tumbleweed for riscv64

Name: tensorflow-lite-devel Distribution: openSUSE Tumbleweed
Version: 2.10.0 Vendor: openSUSE
Release: 3.1 Build date: Mon Oct 23 00:30:54 2023
Group: Development/Languages/Python Build host: i01-ch2b
Size: 239902264 Source RPM: tensorflow-lite-2.10.0-3.1.src.rpm
Packager: https://bugs.opensuse.org
Url: https://www.tensorflow.org/
Summary: Header files of tensorflow
 
TensorFlow is an end-to-end open source platform for machine learning.
The Tensorflow Lite package is a fraction the size of the full tensorflow package
and includes the bare minimum code required to run inferences with TensorFlow Lite
 — primarily the Interpreter Python class. This small package is for when all you
want to do is execute .tflite models and avoid wasting disk space with the large
TensorFlow library.

This package provides necessary headers for the C/C++ Api using TensorFlow Lite.
As well as the static libtensorflow-lite.a for use in your own projects without the
Python interpreter.

Provides

Requires

License

Apache-2.0 AND BSD-2-Clause AND BSD-3-Clause AND MIT AND MPL-2.0

Changelog

* Thu Oct 19 2023 Daniel Garcia Moreno <daniel.garcia@suse.com>
  - Don't use `=` in %python3_install macro parameters to avoid parsing
    problems with future changes of the macro expasion.
    See gh#openSUSE/python-rpm-macros#164
* Mon Jun 05 2023 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Use gcc12 to build since build fails with default gcc (13)
* Tue Sep 20 2022 Ben Greiner <code@bnavigator.de>
  - Update to 2.10.0
    * boo#1203507 (CVE-2022-35934)
  - Breaking Changes
    * Causal attention in keras.layers.Attention and
      keras.layers.AdditiveAttention is now specified in the call()
      method via the use_causal_mask argument (rather than in the
      constructor), for consistency with other layers.
    * Some files in tensorflow/python/training have been moved to
      tensorflow/python/tracking and tensorflow/python/checkpoint.
      Please update your imports accordingly, the old files will be
      removed in Release 2.11.
    * tf.keras.optimizers.experimental.Optimizer will graduate in
      Release 2.11, which means tf.keras.optimizers.Optimizer will
      be an alias of tf.keras.optimizers.experimental.Optimizer. The
      current tf.keras.optimizers.Optimizer will continue to be
      supported as tf.keras.optimizers.legacy.Optimizer,
      e.g.,tf.keras.optimizers.legacy.Adam. Most users won't be
      affected by this change, but please check the API doc if any
      API used in your workflow is changed or deprecated, and make
      adaptions. If you decide to keep using the old optimizer,
      please explicitly change your optimizer to
      tf.keras.optimizers.legacy.Optimizer.
    * RNG behavior change for tf.keras.initializers. Keras
      initializers will now use stateless random ops to generate
      random numbers.
    - Both seeded and unseeded initializers will always generate
      the same values every time they are called (for a given
      variable shape). For unseeded initializers (seed=None), a
      random seed will be created and assigned at initializer
      creation (different initializer instances get different
      seeds).
    - An unseeded initializer will raise a warning if it is reused
      (called) multiple times. This is because it would produce
      the same values each time, which may not be intended.
  - Deprecations
    * The C++ tensorflow::Code and tensorflow::Status will become
      aliases of respectively absl::StatusCode and absl::Status in
      some future release.
    - Use tensorflow::OkStatus() instead of
      tensorflow::Status::OK().
    - Stop constructing Status objects from
      tensorflow::error::Code.
    - One MUST NOT access tensorflow::errors::Code fields.
      Accessing tensorflow::error::Code fields is fine.
      + Use the constructors such as
      tensorflow::errors:InvalidArgument to create status using
      an error code without accessing it.
      + Use the free functions such as
      tensorflow::errors::IsInvalidArgument if needed.
      + In the last resort, use
      e.g.static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT)
      or static_cast<int>(code) for comparisons.
    * tensorflow::StatusOr will also become in the future alias to
      absl::StatusOr, so use StatusOr::value instead of
      StatusOr::ConsumeValueOrDie.
  - Major Features and Improvements
    * tf.lite:
    - New operations supported:
      + tflite SelectV2 now supports 5D.
      + tf.einsum is supported with multiple unknown shapes.
      + tf.unsortedsegmentprod op is supported.
      + tf.unsortedsegmentmax op is supported.
      + tf.unsortedsegmentsum op is supported.
    - Updates to existing operations:
      + tfl.scatter_nd now supports I1 for update arg.
    - Upgrade Flatbuffers v2.0.5 from v1.12.0
    * tf.keras:
    - EinsumDense layer is moved from experimental to core. Its
      import path is moved from
      tf.keras.layers.experimental.EinsumDense to
      tf.keras.layers.EinsumDense.
    - Added tf.keras.utils.audio_dataset_from_directory utility to
      easily generate audio classification datasets from
      directories of .wav files.
    - Added subset="both" support in
      tf.keras.utils.image_dataset_from_directory,tf.keras.utils.text_dataset_from_directory,
      and audio_dataset_from_directory, to be used with the
      validation_split argument, for returning both dataset splits
      at once, as a tuple.
    - Added tf.keras.utils.split_dataset utility to split a
      Dataset object or a list/tuple of arrays into two Dataset
      objects (e.g. train/test).
    - Added step granularity to BackupAndRestore callback for
      handling distributed training failures & restarts. The
      training state can now be restored at the exact epoch and
      step at which it was previously saved before failing.
    - Added tf.keras.dtensor.experimental.optimizers.AdamW. This
      optimizer is similar as the existing
      keras.optimizers.experimental.AdamW, and works in the
      DTensor training use case.
    - Improved masking support for
      tf.keras.layers.MultiHeadAttention.
      + Implicit masks for query, key and value inputs will
      automatically be used to compute a correct attention mask
      for the layer. These padding masks will be combined with
      any attention_mask passed in directly when calling the
      layer. This can be used with tf.keras.layers.Embedding
      with mask_zero=True to automatically infer a correct
      padding mask.
      + Added a use_causal_mask call time arugment to the layer.
      Passing use_causal_mask=True will compute a causal
      attention mask, and optionally combine it with any
      attention_mask passed in directly when calling the layer.
    - Added ignore_class argument in the loss
      SparseCategoricalCrossentropy and metrics IoU and MeanIoU,
      to specify a class index to be ignored during loss/metric
      computation (e.g. a background/void class).
    - Added
      tf.keras.models.experimental.SharpnessAwareMinimization.
      This class implements the sharpness-aware minimization
      technique, which boosts model performance on various tasks,
      e.g., ResNet on image classification.
    * tf.data:
    - Added support for cross-trainer data caching in tf.data
      service. This saves computation resources when concurrent
      training jobs train from the same dataset. See
      (https://www.tensorflow.org/api_docs/python/tf/data/experimental/service#sharing_tfdata_service_with_concurrent_trainers)
      for more details.
    - Added dataset_id to
      tf.data.experimental.service.register_dataset. If provided,
      tf.data service will use the provided ID for the dataset. If
      the dataset ID already exists, no new dataset will be
      registered. This is useful if multiple training jobs need to
      use the same dataset for training. In this case, users
      should call register_dataset with the same dataset_id.
    - Added a new field, inject_prefetch, to
      tf.data.experimental.OptimizationOptions. If it is set to
      True,tf.data will now automatically add a prefetch
      transformation to datasets that end in synchronous
      transformations. This enables data generation to be
      overlapped with data consumption. This may cause a small
      increase in memory usage due to buffering. To enable this
      behavior, set inject_prefetch=True in
      tf.data.experimental.OptimizationOptions.
    - Added a new value to
      tf.data.Options.autotune.autotune_algorithm: STAGE_BASED. If
      the autotune algorithm is set to STAGE_BASED, then it runs a
      new algorithm that can get the same performance with lower
      CPU/memory usage.
    - Added tf.data.experimental.from_list, a new API for creating
      Datasets from lists of elements.
    * tf.distribute:
    - Added tf.distribute.experimental.PreemptionCheckpointHandler
      to handle worker preemption/maintenance and cluster-wise
      consistent error reporting for
      tf.distribute.MultiWorkerMirroredStrategy. Specifically, for
      the type of interruption with advance notice, it
      automatically saves a checkpoint, exits the program without
      raising an unrecoverable error, and restores the progress
      when training restarts.
    * tf.math:
    - Added tf.math.approx_max_k and tf.math.approx_min_k which
      are the optimized alternatives to tf.math.top_k on TPU. The
      performance difference range from 8 to 100 times depending
      on the size of k. When running on CPU and GPU, a
      non-optimized XLA kernel is used.
    * tf.train:
    - Added tf.train.TrackableView which allows users to inspect
      the TensorFlow Trackable object (e.g. tf.Module, Keras
      Layers and models).
    * tf.vectorized_map:
    - Added an optional parameter: warn. This parameter controls
      whether or not warnings will be printed when operations in
      the provided fn fall back to a while loop.
    * XLA:
    - MWMS is now compilable with XLA.
    - Compute Library for the Arm® Architecture (ACL) is supported
      for aarch64 CPU XLA runtime
    * CPU performance optimizations:
    - x86 CPUs: oneDNN bfloat16 auto-mixed precision grappler
      graph optimization pass has been renamed from
      auto_mixed_precision_mkl to
      auto_mixed_precision_onednn_bfloat16. See example usage
      here.
    - aarch64 CPUs: Experimental performance optimizations from
      Compute Library for the Arm® Architecture (ACL) are
      available through oneDNN in the default Linux aarch64
      package (pip install tensorflow).
      + The optimizations are disabled by default.
      + Set the environment variable TF_ENABLE_ONEDNN_OPTS=1 to
      enable the optimizations. Setting the variable to 0 or
      unsetting it will disable the optimizations.
      + These optimizations can yield slightly different numerical
      results from when they are off due to floating-point
      round-off errors from different computation approaches and
      orders.
      + To verify that the optimizations are on, look for a
      message with "oneDNN custom operations are on" in the log.
      If the exact phrase is not there, it means they are off.
  - Bug Fixes and Other Changes
    * New argument experimental_device_ordinal in
      LogicalDeviceConfiguration to control the order of logical
      devices. (GPU only)
    * tf.keras:
    - Changed the TensorBoard tag names produced by the
      tf.keras.callbacks.TensorBoard callback, so that summaries
      logged automatically for model weights now include either a
      /histogram or /image suffix in their tag names, in order to
      prevent tag name collisions across summary type
    * When running on GPU (with cuDNN version 7.6.3 or
      later),tf.nn.depthwise_conv2d backprop to filter (and
      therefore also tf.keras.layers.DepthwiseConv2D) now operate
      deterministically (and tf.errors.UnimplementedError is no
      longer thrown) when op-determinism has been enabled via
      tf.config.experimental.enable_op_determinism. This closes
      issue 47174.
    * tf.random
    - Added tf.random.experimental.stateless_shuffle, a stateless
      version of tf.random.shuffle.
  - Security
    * Fixes a CHECK failure in tf.reshape caused by overflows
      (CVE-2022-35934)
    * Fixes a CHECK failure in SobolSample caused by missing
      validation (CVE-2022-35935)
    * Fixes an OOB read in Gather_nd op in TF Lite (CVE-2022-35937)
    * Fixes a CHECK failure in TensorListReserve caused by missing
      validation (CVE-2022-35960)
    * Fixes an OOB write in Scatter_nd op in TF Lite
      (CVE-2022-35939)
    * Fixes an integer overflow in RaggedRangeOp (CVE-2022-35940)
    * Fixes a CHECK failure in AvgPoolOp (CVE-2022-35941)
    * Fixes a CHECK failures in UnbatchGradOp (CVE-2022-35952)
    * Fixes a segfault TFLite converter on per-channel quantized
      transposed convolutions (CVE-2022-36027)
    * Fixes a CHECK failures in AvgPool3DGrad (CVE-2022-35959)
    * Fixes a CHECK failures in FractionalAvgPoolGrad
      (CVE-2022-35963)
    * Fixes a segfault in BlockLSTMGradV2 (CVE-2022-35964)
    * Fixes a segfault in LowerBound and UpperBound (CVE-2022-35965)
    * Fixes a segfault in QuantizedAvgPool (CVE-2022-35966)
    * Fixes a segfault in QuantizedAdd (CVE-2022-35967)
    * Fixes a CHECK fail in AvgPoolGrad (CVE-2022-35968)
    * Fixes a CHECK fail in Conv2DBackpropInput (CVE-2022-35969)
    * Fixes a segfault in QuantizedInstanceNorm (CVE-2022-35970)
    * Fixes a CHECK fail in FakeQuantWithMinMaxVars (CVE-2022-35971)
    * Fixes a segfault in Requantize (CVE-2022-36017)
    * Fixes a segfault in QuantizedBiasAdd (CVE-2022-35972)
    * Fixes a CHECK fail in FakeQuantWithMinMaxVarsPerChannel
      (CVE-2022-36019)
    * Fixes a segfault in QuantizedMatMul (CVE-2022-35973)
    * Fixes a segfault in QuantizeDownAndShrinkRange
      (CVE-2022-35974)
    * Fixes segfaults in QuantizedRelu and QuantizedRelu6
      (CVE-2022-35979)
    * Fixes a CHECK fail in FractionalMaxPoolGrad (CVE-2022-35981)
    * Fixes a CHECK fail in RaggedTensorToVariant (CVE-2022-36018)
    * Fixes a CHECK fail in QuantizeAndDequantizeV3 (CVE-2022-36026)
    * Fixes a segfault in SparseBincount (CVE-2022-35982)
    * Fixes a CHECK fail in Save and SaveSlices (CVE-2022-35983)
    * Fixes a CHECK fail in ParameterizedTruncatedNormal
      (CVE-2022-35984)
    * Fixes a CHECK fail in LRNGrad (CVE-2022-35985)
    * Fixes a segfault in RaggedBincount (CVE-2022-35986)
    * Fixes a CHECK fail in DenseBincount (CVE-2022-35987)
    * Fixes a CHECK fail in tf.linalg.matrix_rank (CVE-2022-35988)
    * Fixes a CHECK fail in MaxPool (CVE-2022-35989)
    * Fixes a CHECK fail in Conv2DBackpropInput (CVE-2022-35999)
    * Fixes a CHECK fail in EmptyTensorList (CVE-2022-35998)
    * Fixes a CHECK fail in tf.sparse.cross (CVE-2022-35997)
    * Fixes a floating point exception in Conv2D (CVE-2022-35996)
    * Fixes a CHECK fail in AudioSummaryV2 (CVE-2022-35995)
    * Fixes a CHECK fail in CollectiveGather (CVE-2022-35994)
    * Fixes a CHECK fail in SetSize (CVE-2022-35993)
    * Fixes a CHECK fail in TensorListFromTensor (CVE-2022-35992)
    * Fixes a CHECK fail in TensorListScatter and
      TensorListScatterV2 (CVE-2022-35991)
    * Fixes a CHECK fail in
      FakeQuantWithMinMaxVarsPerChannelGradient (CVE-2022-35990)
    * Fixes a CHECK fail in FakeQuantWithMinMaxVarsGradient
      (CVE-2022-36005)
    * Fixes a CHECK fail in tf.random.gamma (CVE-2022-36004)
    * Fixes a CHECK fail in RandomPoissonV2 (CVE-2022-36003)
    * Fixes a CHECK fail in Unbatch (CVE-2022-36002)
    * Fixes a CHECK fail in DrawBoundingBoxes (CVE-2022-36001)
    * Fixes a CHECK fail in Eig (CVE-2022-36000)
    * Fixes a null dereference on MLIR on empty function attributes
      (CVE-2022-36011)
    * Fixes an assertion failure on MLIR empty edge names
      (CVE-2022-36012)
    * Fixes a null-dereference in
      mlir::tfg::GraphDefImporter::ConvertNodeDef (CVE-2022-36013)
    * Fixes a null-dereference in mlir::tfg::TFOp::nameAttr
      (CVE-2022-36014)
    * Fixes an integer overflow in math ops (CVE-2022-36015)
    * Fixes a CHECK-fail in
      tensorflow::full_type::SubstituteFromAttrs (CVE-2022-36016)
    * Fixes an OOB read in Gather_nd op in TF Lite Micro
      (CVE-2022-35938)
* Fri May 27 2022 Ben Greiner <code@bnavigator.de>
  - tensorflow2 has been removed from Tumbleweed. Provide a separate
    Tensorflow Lite package in version 2.9.1
  - Now includes the tflite_runtime python3 package
  - Add tensorflow-lite-cmake-find-python.patch
* Fri Feb 04 2022 Ben Greiner <code@bnavigator.de>
  - restore larger memory per job constraint
* Fri Feb 04 2022 Ben Greiner <code@bnavigator.de>
  - Update to 2.7.1 -- boo#1195545 security update
    * Fixes a floating point division by 0 when executing convolution
      operators (CVE-2022-21725)
    * Fixes a heap OOB read in shape inference for ReverseSequence
      (CVE-2022-21728)
    * Fixes a heap OOB access in Dequantize (CVE-2022-21726)
    * Fixes an integer overflow in shape inference for Dequantize
      (CVE-2022-21727)
    * Fixes a heap OOB access in FractionalAvgPoolGrad
      (CVE-2022-21730)
    * Fixes an overflow and divide by zero in UnravelIndex
      (CVE-2022-21729)
    * Fixes a type confusion in shape inference for ConcatV2
      (CVE-2022-21731)
    * Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
    * Fixes an OOM due to integer overflow in StringNGrams
      (CVE-2022-21733)
    * Fixes more issues caused by incomplete validation in boosted
      trees code (CVE-2021-41208)
    * Fixes an integer overflows in most sparse component-wise ops
      (CVE-2022-23567)
    * Fixes an integer overflows in AddManySparseToTensorsMap
      (CVE-2022-23568)
    * Fixes a number of CHECK-failures in MapStage (CVE-2022-21734)
    * Fixes a division by zero in FractionalMaxPool (CVE-2022-21735)
    * Fixes a number of CHECK-fails when building invalid/overflowing
      tensor shapes (CVE-2022-23569)
    * Fixes an undefined behavior in SparseTensorSliceDataset
      (CVE-2022-21736)
    * Fixes an assertion failure based denial of service via faulty
      bin count operations (CVE-2022-21737)
    * Fixes a reference binding to null pointer in QuantizedMaxPool
      (CVE-2022-21739)
    * Fixes an integer overflow leading to crash in
      SparseCountSparseOutput (CVE-2022-21738)
    * Fixes a heap overflow in SparseCountSparseOutput
      (CVE-2022-21740)
    * Fixes an FPE in BiasAndClamp in TFLite (CVE-2022-23557)
    * Fixes an FPE in depthwise convolutions in TFLite
      (CVE-2022-21741)
    * Fixes an integer overflow in TFLite array creation
      (CVE-2022-23558)
    * Fixes an integer overflow in TFLite (CVE-2022-23559)
    * Fixes a dangerous OOB write in TFLite (CVE-2022-23561)
    * Fixes a vulnerability leading to read and write outside of
      bounds in TFLite (CVE-2022-23560)
    * Fixes a set of vulnerabilities caused by using insecure
      temporary files (CVE-2022-23563)
    * Fixes an integer overflow in Range resulting in undefined
      behavior and OOM (CVE-2022-23562)
    * Fixes a vulnerability where missing validation causes
      tf.sparse.split to crash when axis is a tuple (CVE-2021-41206)
    * Fixes a CHECK-fail when decoding resource handles from proto
      (CVE-2022-23564)
    * Fixes a CHECK-fail with repeated AttrDef (CVE-2022-23565)
    * Fixes a heap OOB write in Grappler (CVE-2022-23566)
    * Fixes a CHECK-fail when decoding invalid tensors from proto
      (CVE-2022-23571)
    * Fixes a null-dereference when specializing tensor type
      (CVE-2022-23570)
    * Fixes a crash when type cannot be specialized (CVE-2022-23572)
    * Fixes a heap OOB read/write in SpecializeType (CVE-2022-23574)
    * Fixes an unitialized variable access in AssignOp
      (CVE-2022-23573)
    * Fixes an integer overflow in
      OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)
    * Fixes an integer overflow in
      OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)
    * Fixes a null dereference in GetInitOp (CVE-2022-23577)
    * Fixes a memory leak when a graph node is invalid
      (CVE-2022-23578)
    * Fixes an abort caused by allocating a vector that is too large
      (CVE-2022-23580)
    * Fixes multiple CHECK-failures during Grappler's
      IsSimplifiableReshape (CVE-2022-23581)
    * Fixes multiple CHECK-failures during Grappler's
      SafeToRemoveIdentity (CVE-2022-23579)
    * Fixes multiple CHECK-failures in TensorByteSize
      (CVE-2022-23582)
    * Fixes multiple CHECK-failures in binary ops due to type
      confusion (CVE-2022-23583)
    * Fixes a use after free in DecodePng kernel (CVE-2022-23584)
    * Fixes a memory leak in decoding PNG images (CVE-2022-23585)
    * Fixes multiple CHECK-fails in function.cc (CVE-2022-23586)
    * Fixes multiple CHECK-fails due to attempting to build a
      reference tensor (CVE-2022-23588)
    * Fixes an integer overflow in Grappler cost estimation of crop
      and resize operation (CVE-2022-23587)
    * Fixes a null pointer dereference in Grappler's IsConstant
      (CVE-2022-23589)
    * Fixes a CHECK failure in constant folding (CVE-2021-41197)
    * Fixes a stack overflow due to self-recursive function in
      GraphDef (CVE-2022-23591)
    * Fixes a crash due to erroneous StatusOr (CVE-2022-23590)
    * Fixes multiple crashes and heap OOB accesses in TFG dialect
      (MLIR) (CVE-2022-23594)
    * Fixes a null pointer dereference in BuildXlaCompilationCache
      (XLA) (CVE-2022-23595)
    * Updates icu to 69.1 to handle CVE-2020-10531
* Tue Feb 01 2022 Ben Greiner <code@bnavigator.de>
  - Remove URLs from github zip archives for xnnpack transitive
    dependencies: The GitHub archiver produces unreliable files
* Sat Jan 22 2022 Ben Greiner <code@bnavigator.de>
  - Update to 2.7.0
    * Big changelog: at
      https://github.com/tensorflow/tensorflow/releases/tag/v2.7.0
  - Security references:
    * Fixes a code injection issue in saved_model_cli
      (CVE-2021-41228)
    * Fixes a vulnerability due to use of uninitialized value in
      Tensorflow (CVE-2021-41225)
    * Fixes a heap OOB in FusedBatchNorm kernels (CVE-2021-41223)
    * Fixes an arbitrary memory read in ImmutableConst
      (CVE-2021-41227)
    * Fixes a heap OOB in SparseBinCount (CVE-2021-41226)
    * Fixes a heap OOB in SparseFillEmptyRows (CVE-2021-41224)
    * Fixes a segfault due to negative splits in SplitV
      (CVE-2021-41222)
    * Fixes segfaults and vulnerabilities caused by accesses to
      invalid memory during shape inference in Cudnn* ops
      (CVE-2021-41221)
    * Fixes a null pointer exception when Exit node is not preceded
      by Enter op (CVE-2021-41217)
    * Fixes an integer division by 0 in tf.raw_ops.AllToAll
      (CVE-2021-41218)
    * Fixes a use after free and a memory leak in CollectiveReduceV2
      (CVE-2021-41220)
    * Fixes an undefined behavior via nullptr reference binding in
      sparse matrix multiplication (CVE-2021-41219)
    * Fixes a heap buffer overflow in Transpose (CVE-2021-41216)
    * Prevents deadlocks arising from mutually recursive tf.function
      objects (CVE-2021-41213)
    * Fixes a null pointer exception in DeserializeSparse
      (CVE-2021-41215)
    * Fixes an undefined behavior arising from reference binding to
      nullptr in tf.ragged.cross (CVE-2021-41214)
    * Fixes a heap OOB read in tf.ragged.cross (CVE-2021-41212)
    * Fixes a heap OOB in shape inference for QuantizeV2
      (CVE-2021-41211)
    * Fixes a heap OOB read in all tf.raw_ops.QuantizeAndDequantizeV*
      ops (CVE-2021-41205)
    * Fixes an FPE in ParallelConcat (CVE-2021-41207)
    * Fixes FPE issues in convolutions with zero size filters
      (CVE-2021-41209)
    * Fixes a heap OOB read in tf.raw_ops.SparseCountSparseOutput
      (CVE-2021-41210)
    * Fixes vulnerabilities caused by incomplete validation in
      boosted trees code (CVE-2021-41208)
    * Fixes vulnerabilities caused by incomplete validation of shapes
      in multiple TF ops (CVE-2021-41206)
    * Fixes a segfault produced while copying constant resource
      tensor (CVE-2021-41204)
    * Fixes a vulnerability caused by unitialized access in
      EinsumHelper::ParseEquation (CVE-2021-41201)
    * Fixes several vulnerabilities and segfaults caused by missing
      validation during checkpoint loading (CVE-2021-41203)
    * Fixes an overflow producing a crash in tf.range
      (CVE-2021-41202)
    * Fixes an overflow producing a crash in tf.image.resize when
      size is large (CVE-2021-41199)
    * Fixes an overflow producing a crash in tf.tile when tiling
      tensor is large (CVE-2021-41198)
    * Fixes a vulnerability produced due to incomplete validation in
      tf.summary.create_file_writer (CVE-2021-41200)
    * Fixes multiple crashes due to overflow and CHECK-fail in ops
      with large tensor shapes (CVE-2021-41197)
    * Fixes a crash in max_pool3d when size argument is 0 or negative
      (CVE-2021-41196)
    * Fixes a crash in tf.math.segment_* operations (CVE-2021-41195)
    * Updates curl to 7.78.0 to handle CVE-2021-22922,
      CVE-2021-22923, CVE-2021-22924, CVE-2021-22925, and
      CVE-2021-22926.
  - This drops support for Python 3.6 and thus for SLE/Leap 15
    See also https://code.opensuse.org/leap/features/issue/35
  - Closes boo#1195295
    * Note that tensorflow2 (non-lite) will be removed from
      Tumbleweed soon if there are no volunteers, see leap feature
      issue above.
  - Have to migrate tensorflow-lite build to CMake as old Makefile
    was dropped
  - Drop patches no longer necessary or applicable
    * tensorflow-2.6.0-remove-weakref.patch
    * tensorflow-2.6.0-fix-lite.patch
    * tensorflow-2.6.0-tf-keras-hdf5-3.patch
    * tensorflow-2.6.0-removed-clog-build-as-included-in-cpuinfo.patch
    * tensorflow-2.6.0-numpy-tensor-small.patch
  - fix double nested unpacking and refresh patches, migrate to -p1
    * tensorflow-2.6.0-removed-external-toolchains.patch
    * tensorflow-2.6.0-compile-with-protobuf-3.16.patch
  - Add #tensorflow-2.7.0-fix-lite.patch
    * https://github.com/tensorflow/tensorflow/commit/fb1dcbd9
    * gh#tensorflow/tensorflow#54216
  - Have to use grpc and upb from bazelcache, pulls in go
    * Add tensorflow-2.7.0-go_host_sdk.patch -- use system SDK
      instead of downloading a binary blob
* Tue Jan 11 2022 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Remove more python dependencies for tensorflow2-lite
* Mon Jan 10 2022 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - tensorflow2-lite version does not need all the python dependencies
    listed for tensorflow2
* Fri Jan 07 2022 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Leap 15.x / Backports: Do not build non-Lite versions since python3-numpy
    and python3-scipy are too old for Keras/TF2
* Tue Nov 09 2021 Christian Goll <cgoll@suse.com>
  - updated to 2.6.2 which is bug fix release which just fixes an issue where
    keras, tensorflow_estimator and tensorboard were missing proper upper bounds
    and resulted in broken installs after Keras 2.7 release for all packages in
    TensorFlow ecosystem
  - Fixes from 2.6.1 (boo#1192447):
    * Fixes a code injection issue in saved_model_cli (CVE-2021-41228)
    * Fixes a vulnerability due to use of uninitialized value in Tensorflow (CVE-2021-41225)
    * Fixes a heap OOB in FusedBatchNorm kernels (CVE-2021-41223)
    * Fixes an arbitrary memory read in ImmutableConst (CVE-2021-41227)
    * Fixes a heap OOB in SparseBinCount (CVE-2021-41226)
    * Fixes a heap OOB in SparseFillEmptyRows (CVE-2021-41224)
    * Fixes a segfault due to negative splits in SplitV (CVE-2021-41222)
    * Fixes segfaults and vulnerabilities caused by accesses to invalid memory
      during shape inference in Cudnn* ops (CVE-2021-41221)
    * Fixes a null pointer exception when Exit node is not preceded by Enter op (CVE-2021-41217)
    * Fixes an integer division by 0 in tf.raw_ops.AllToAll (CVE-2021-41218)
    * Fixes a use after free and a memory leak in CollectiveReduceV2 (CVE-2021-41220)
    * Fixes an undefined behavior via nullptr reference binding in sparse matrix
      multiplication (CVE-2021-41219)
    * Fixes a heap buffer overflow in Transpose (CVE-2021-41216)
    * Prevents deadlocks arising from mutually recursive tf.function objects (CVE-2021-41213)
    * Fixes a null pointer exception in DeserializeSparse (CVE-2021-41215)
    * Fixes an undefined behavior arising from reference binding to nullptr in
      tf.ragged.cross (CVE-2021-41214)
    * Fixes a heap OOB read in tf.ragged.cross (CVE-2021-41212)
    * Fixes a heap OOB in shape inference for QuantizeV2 (CVE-2021-41211)
    * Fixes a heap OOB read in all tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)
    * Fixes an FPE in ParallelConcat (CVE-2021-41207)
    * Fixes FPE issues in convolutions with zero size filters (CVE-2021-41209)
    * Fixes a heap OOB read in tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)
    * Fixes vulnerabilities caused by incomplete validation in boosted trees code (CVE-2021-41208)
    * Fixes vulnerabilities caused by incomplete validation of shapes in multiple TF ops (CVE-2021-41206)
    * Fixes a segfault produced while copying constant resource tensor (CVE-2021-41204)
    * Fixes a vulnerability caused by unitialized access in EinsumHelper::ParseEquation (CVE-2021-41201)
    * Fixes several vulnerabilities and segfaults caused by missing validation
      during checkpoint loading (CVE-2021-41203)
    * Fixes an overflow producing a crash in tf.range (CVE-2021-41202)
    * Fixes an overflow producing a crash in tf.image.resize when size is large (CVE-2021-41199)
    * Fixes an overflow producing a crash in tf.tile when tiling tensor is large (CVE-2021-41198)
    * Fixes a vulnerability produced due to incomplete validation in
      tf.summary.create_file_writer (CVE-2021-41200)
    * Fixes multiple crashes due to overflow and CHECK-fail in ops with large
      tensor shapes (CVE-2021-41197)
    * Fixes a crash in max_pool3d when size argument is 0 or negative (CVE-2021-41196)
    * Fixes a crash in tf.math.segment_* operations (CVE-2021-41195)
* Sat Oct 23 2021 Egbert Eich <eich@suse.com>
  - Make sure tensorflow/core/public/version.h is installed in the
    'lite' version (bsc#1191805).
* Fri Sep 24 2021 Ben Greiner <code@bnavigator.de>
  - Add missing python requirements -- boo#1190856
* Wed Sep 01 2021 Egbert Eich <eich@suse.com>
  - Limit BuildRequires for bazel-skylib-source to versions >= 1.0.3.
* Thu Aug 19 2021 Fusion Future <qydwhotmail@gmail.com>
  - Update to 2.6.0
    Major changes are:
    * Keras been split into a separate PIP package (keras), and its
      code has been moved to the GitHub repositorykeras-team/keras.
      The API endpoints for tf.keras stay unchanged, but are now
      backed by the keras PIP package. The existing code in
      tensorflow/python/keras is a staled copy and will be removed in
      future release (2.7). Please remove any imports to
      tensorflow.python.keras and replace them with public tf.keras
      API instead.
    * tf.train.experimental.enable_mixed_precision_graph_rewrite is
      removed, as the API only works in graph mode and is not
      customizable. The function is still accessible under
      tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite,
      but it is recommended to use the Keras mixed precision API
      instead.
    * tf.lite: Remove experimental.nn.dynamic_rnn,
      experimental.nn.TfLiteRNNCell and
      experimental.nn.TfLiteLSTMCell since they're no
      longer supported. It's recommended to just use keras lstm
      instead.
    * tf.keras: The methods Model.to_yaml() and
      keras.models.model_from_yaml have been replaced to raise a
      RuntimeError as they can be abused to cause arbitrary code
      execution. It is recommended to use JSON serialization instead
      of YAML, or, a better alternative, serialize to H5.
  - Major changes from 2.5.x:
    * Support for Python3.9 has been added.
    * The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed
      to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its
      effect.
  - Fixed multiple CVEs (boo#1189423):
    * CVE-2021-37635
    * CVE-2021-37636
    * CVE-2021-37637
    * CVE-2021-37638
    * CVE-2021-37639
    * CVE-2021-37640
    * CVE-2021-37642
    * CVE-2021-37641
    * CVE-2021-37644
    * CVE-2021-37643
    * CVE-2021-37645
    * CVE-2021-37646
    * CVE-2021-37647
    * CVE-2021-37648
    * CVE-2021-37649
    * CVE-2021-37650
    * CVE-2021-37651
    * CVE-2021-37652
    * CVE-2021-37653
    * CVE-2021-37654
    * CVE-2021-37655
    * CVE-2021-37656
    * CVE-2021-37657
    * CVE-2021-37658
    * CVE-2021-37659
    * CVE-2021-37660
    * CVE-2021-37661
    * CVE-2021-37662
    * CVE-2021-37664
    * CVE-2021-37663
    * CVE-2021-37665
    * CVE-2021-37666
    * CVE-2021-37667
    * CVE-2021-37668
    * CVE-2021-37669
    * CVE-2021-37670
    * CVE-2021-37671
    * CVE-2021-37672
    * CVE-2021-37673
    * CVE-2021-37674
    * CVE-2021-37676
    * CVE-2021-37675
    * CVE-2021-37677
    * CVE-2021-37678
    * CVE-2021-37679
    * CVE-2021-37680
    * CVE-2021-37681
    * CVE-2021-37682
    * CVE-2021-37683
    * CVE-2021-37684
    * CVE-2021-37686
    * CVE-2021-37685
    * CVE-2021-37687
    * CVE-2021-37688
    * CVE-2021-37689
    * CVE-2021-37691
    * CVE-2021-37692
    * CVE-2021-37690
  - Updated sources:
    * abseil-cpp.tar.gz
    * cpuinfo.zip
    * dill-0.3.2.zip
    * eigen.tar.gz
    * google-cloud-cpp.tar.gz
    * libxsmm_1.14.tar.gz
    * llvm.tar.gz
    * oneDNN.tar.gz
    * rules_cc.tar.gz
    * rules_closure.tar.gz
    * rules_docker-0.18.0.tar.gz
    * ruy.zip
    * tblib-1.7.0.tar.gz
  - Added sources:
    * ComputeLibrary.tar.gz
    * oneDNN-v2.3-rc2.tar.gz
    * platforms-0.0.2.tar.gz
    * rules_proto.tar.gz
    * tf_runtime.tar.gz
    * tf_toolchains.tar.gz
  - Removed sources:
    * kafka-v0.11.5.tar.gz
  - Add "tensorflow-2.6.0" prefix to existing patches to indicate
    that patches are likely to be only applicable to a specific
    version.
    * fix-lite.patch -> tensorflow-2.6.0-fix-lite.patch
    * numpy-tensor-small.patch -> tensorflow-2.6.0-numpy-tensor-small.patch
    * removed-clog-build-as-included-in-cpuinfo.patch -> tensorflow-2.6.0-removed-clog-build-as-included-in-cpuinfo.patch
    * removed-external-toolchains.patch -> tensorflow-2.6.0-removed-external-toolchains.patch
    * remove-weakref.patch -> tensorflow-2.6.0-remove-weakref.patch
    * tf-keras-hdf5-3.patch -> tensorflow-2.6.0-tf-keras-hdf5-3.patch
  - Rebase all existing patches.
  - Add tensorflow-2.6.0-compile-with-protobuf-3.16.patch to fix
    build error with protobuf >= 3.16.0. (boo#1186860)
    (https://github.com/protocolbuffers/protobuf/pull/8354)
  - Update bazel version requirement to 3.7.2.
  - Drop pcre-devel build requirement as it is not used anymore.
  - Drop --incompatible_no_support_tools_in_action_inputs=false as it
    is removed in bazel >= 3.6.
* Wed May 19 2021 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Update _constraints to use host with 'asimdrdm' cpu flag to avoid
    slow CPU and be scheduled on faster systems
* Wed Apr 14 2021 Ferdinand Thiessen <rpm@fthiessen.de>
  - Update to version 2.4.1
    * Bugfixes
    * Drops requirement of AVX2
* Tue Apr 06 2021 Ben Greiner <code@bnavigator.de>
  - Don't BuildRequire keras_applications. Tensorflow provides it
    itself: https://github.com/tensorflow/tensorflow/commit/23c3bdaa
  - These were discovered by Keras test suite:
    * add numpy-tensor-small.patch for Numpy >= 1.20
      gh#tensorflow/tensorflow#47691
    * add tf-keras-hdf5-3.patch for hdf5 >= 3.0
      gh#tensorflow/tensorflow#44467
* Thu Feb 18 2021 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Generate and install pkgconfig files for tensorflow-lite and
    tensorflow (non-hpc)
* Wed Jan 27 2021 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Do not install bazel tools to build Lite version. This will allow
    to build for armv7 where bazel 3.x is not available - boo#1178564
* Fri Jan 15 2021 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Fix packaging for libiomp5
* Mon Jan 11 2021 Dirk Müller <dmueller@suse.com>
  - build verbose to not fail on the obs stall detection
* Fri Jan 08 2021 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - libiomp5 is x86_64 only
* Wed Jan 06 2021 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Fix build on aarch64 and on hpc variants
* Wed Oct 21 2020 Christian Goll <cgoll@suse.com>
  - Updated to version 2.4.0 which fixes several bugs (bsc#1173128)
    (bsc#1173314)i, (bsc#1179455) and (bsc#1178287)
  - Security fixes for CVE-2020-26266, CVE-2020-26267, CVE-2020-26268,
    CVE-2020-26270 and CVE-2020-26271
  - updated sources:
    * abseil-cpp.tar.gz
    * eigen.tar.gz
    * gemmlowp.zip
    * googleapis.zip
    * llvm.tar.gz
  - added sources:
    * DouraFFT.tar.gz
    * cpuinfo.zip
    * dill-0.3.1.1.tar.gz (aarch64 only)
    * dlpack.tar.gz
    * oneDNN.tar.gz
    * openmp-10.0.1.src.tar.xz
    * python-license-astunparse
    * re2.tar.gz
    * rules_android.zip
    * rules_cc.tar.gz
    * rules_docker-0.15.0.tar.gz
    * ruy.zip
    * sobol_data.tar.gz
    * tblib-1.3.2.tar.gz (aarch64 only)
    * typing_extensions-3.7.4.2.tar.gz (aarch64 only)
  - removed sources:
    * boring_ssl.tar.gz
    * cub_1.8.0.zip
    * mkl-v0.21.2.tar.gz
    * pybind11-v2.3.0.tar.gz
    * right-json-location.patch
    * rules_docker.tar.gz
  - removed patches:
    * Provide-overload-to-cope-with-const-ness-change-of-N.patch
    * fix-google-absl-memory.patch
    * json-feature-name.patch
    * libjpeg_turbo-name.patch
    * removed-docker-tools.patch
  - added patches:
    * removed-clog-build-as-included-in-cpuinfo.patch
    * removed-external-toolchains.patch
  - Major changes are:
    * tf.distribute introduces experimental support for asynchronous training of
      models via the tf.distribute.experimental.ParameterServerStrategy API.
    * MultiWorkerMirroredStrategy is now a stable API and is no longer considered
      experimental.
    * Introduces experimental support for a new module named tf.experimental.numpy
      which is a NumPy-compatible API for writing TF programs.
    * A major refactoring of the internals of the Keras Functional API has been
      completed, that should improve the reliability, stability, and performance
      of constructing Functional models.
    * Keras mixed precision API tf.keras.mixed_precision is no longer experimental
      and allows the use of 16-bit floating point formats during training,
      improving performance by up to 3x on GPUs and 60% on TPUs.
    * TensorFlow Profiler now supports profiling MultiWorkerMirroredStrategy and
      tracing multiple workers using the sampling mode API.
* Tue Oct 13 2020 Christian Goll <cgoll@suse.com>
  - fixed hpc flavor and Leap15.2 builds
* Mon Sep 28 2020 Christian Goll <cgoll@suse.com>
  - updated to 2.1.2 with following fixes (bsc#1177022):
    * Fixes an undefined behavior causing a segfault in tf.raw_ops.Switch (CVE-2020-15190)
    * Fixes three vulnerabilities in conversion to DLPack format
      (CVE-2020-15191, CVE-2020-15192, CVE-2020-15193)
    * Fixes two vulnerabilities in SparseFillEmptyRowsGrad
      (CVE-2020-15194, CVE-2020-15195)
    * Fixes an integer truncation vulnerability in code using the
      work sharder API (CVE-2020-15202)
    * Fixes a format string vulnerability in tf.strings.as_string (CVE-2020-15203)
    * Fixes segfault raised by calling session-only ops in eager mode (CVE-2020-15204)
    * Fixes data leak and potential ASLR violation from tf.raw_ops.StringNGrams (CVE-2020-15205)
    * Fixes segfaults caused by incomplete SavedModel validation (CVE-2020-15206)
    * Fixes a data corruption due to a bug in negative indexing support in TFLite (CVE-2020-15207)
    * Fixes a data corruption due to dimension mismatch in TFLite (CVE-2020-15208)
    * Fixes several vulnerabilities in TFLite saved model format
      (CVE-2020-15209, CVE-2020-15210, CVE-2020-15211)
  - using fft2d.tgz instead of fft.tar.gz
  - removed fft.tar.gz
* Mon Sep 14 2020 Christian Goll <cgoll@suse.com>
  - fixed json-feature-name.patch for leap15.2 builds
* Thu Sep 10 2020 Christian Goll <cgoll@suse.com>
  - updated disk constraints, as sometimes the build fails with too
    low disk space
* Wed Sep 09 2020 Christian Goll <cgoll@suse.com>
  - Package C-headers for standard tensorflow (boo#1175789)
  - fixed build gcc10.1 errors for Tumbleweed with following
    upstream patch:
    * added file Provide-overload-to-cope-with-const-ness-change-of-N.patch
* Tue Sep 08 2020 Christian Goll <cgoll@suse.com>
  - Package header files for Tensoflow2 Lite - boo#1175099
* Thu Sep 03 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Revert memoryperjob constraint support and use again
    %limit_build macro to avoid OOM errors
* Thu Aug 20 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Lower memoryperjob to 1300 MB (as done for tensorflow)
* Thu Aug 20 2020 Martin Liška <mliska@suse.cz>
  - Use memoryperjob constraint instead of %limit_build macro.
* Thu Jun 25 2020 Christian Goll <cgoll@suse.com>
  - fixed build with json_cpp 1.9.3 (bsc#1173314)
* Fri Jun 19 2020 Christian Goll <cgoll@suse.com>
  - fixed local CUDA builds
* Tue Jun 16 2020 Christian Goll <cgoll@suse.com>
  - updated to 2.1.1 which is a bug fix release mostly for external
    sources which are not part of this package (sqlite,libjpeg-turbo,
    Apache Spark)
    * Fixes a versioning bug which causes Keras layers from TF 1.x to
    be used instead of those from TF 2.x
* Mon May 25 2020 Christian Goll <cgoll@suse.com>
  - fixed broken builds which were caused due to missing dependency
    on @com_google_absl//absl/strings in various BUILD files
  - added patch: fix-google-absl-memory.patch
* Tue Apr 07 2020 Christian Goll <cgoll@suse.com>
  - added mkl-ddn as source and do not use system mkl-dnn (bsc#1168839)
  - removed patches:
    * fixed-mkl-sgemm-call.patch
    * added-mkl_dnn-as-syslib.patch
  - added source:
    mkl-v0.21.2.tar.gz
* Fri Mar 27 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - tensorflow2-lite-devel does not requires libtensorflow*
* Thu Mar 26 2020 Christian Goll <cgoll@suse.com>
  - removed hpc-mvapich2 build (bsc#1167735)
* Tue Mar 24 2020 Bernhard Wiedemann <bwiedemann@suse.com>
  - Use pip install --no-compile (boo#1094323)
* Fri Mar 20 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Lite flavor should not provide python3-tensorflow nor tensorflow
* Fri Mar 13 2020 Christian Goll <cgoll@suse.com>
  - removed sources of bazel sources and replaced them by internal packages
    * rules-cc.zip removed
    * bazel-toolchains.tar.gz removed
    * bazel-skylib.0.8.0.tar.gz removed
* Mon Mar 09 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Lite flavor should not provide "tensorflow", otherwise
    tensorlfow2-devel and tensorlfow2-lite-devel conflict and break
    armnn
* Thu Mar 05 2020 Christian Goll <cgoll@suse.com>
  - added Provides: tensorflow, so that Kerase works with this package
    and fixed Leap 15.2 build
* Fri Feb 28 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Fix name for libtensorflow* sub-packages
* Thu Feb 20 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - openSUSE has no CUDA package, so disable cuda build for
    openSUSE
* Fri Feb 14 2020 Christian Goll <cgoll@suse.com>
  - addding changes for CUDA builds
* Thu Feb 13 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Add 'Provides' only for hpc flavors, otherwise it matches the
    package name
* Tue Feb 11 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Add provides/conflicts to avoid to install tensorflow and
    tensorflow2 as some files are provided by both packages
* Wed Feb 05 2020 Christian Goll <cgoll@suse.com>
  -  removed mkl-dnn as sourc and force usage of system mkl-dnn
    for x86_64 builds
    * removed file mkl-dnn-v021.2.tar.gz
    * added patch: added-mkl_dnn-as-syslib.patch
    * added patch: fixed-mkl-sgemm-call.patch
* Sun Feb 02 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Add 1.25.0 as minimal version for grpc-devel
* Fri Jan 31 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Add 3.1.5 as a minimal version for double-conversion-devel
    (2.0.1 from SLE15SP2/Leap15.2 is too old)
  - Lower required version for protobuf (3.9.1 from SLE15SP2/Leap15.2
    is fine)
* Thu Jan 30 2020 Christian Goll <cgoll@suse.com>
  - removed AVX2 flavor, this should be fixed via mkl-dnn
* Thu Jan 30 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Fix build on hpc targets
* Tue Jan 28 2020 Christian Goll <cgoll@suse.com>
  - added shared library packages libtensorflow2,
    libtensorflow_cc2 and libtensorflow_framework2
  - removed the AWS sdk support as this forces a SEGFAULT
    * remobed file aws-sdk-cpp-1.5.8.tar.gz
  - dropped following source files as they are not needed any more
    * removed file backports.weakref-1.0rc1.tar.gz
    * removed file gettid.patch
    * removed file grpc-v1.24.2.gz
    * removed file libjpeg-turbo-2.0.0.tar.gz
    * removed file nsync_1.20.0.tar.gz
* Tue Jan 28 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Do not try to install *.pb.* files in Lite flavor
* Tue Jan 28 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Define package name at 'tensorflow2' instead of 'tensorflow'
* Fri Jan 24 2020 Guillaume GARDET <guillaume.gardet@opensuse.org>
  - Generate *.pb.* files and package them, to be used by ArmNN
  - Do not build on %ix86
  - Do not build %arm, except for Lite flavor
* Thu Jan 23 2020 Christian Goll <cgoll@suse.com>
  - updated to tensorflow 2.1.0 which is a stable release and has
    following breaking changes:
    * Deletes Operation.traceback_with_start_lines for which we
      know of no usages.
    * Removed id from tf.Tensor.__repr__() as id is not useful other
      than internal debugging.
    * Some tf.assert_* methods now raise assertions at operation
      creation time if the input tensors' values are known at that
      time, not during the session.run(). This only changes behavior
      when the graph execution would have resulted in an error. When
      this happens, a noop is returned and the input tensors are
      marked non-feedable. In other words, if they are used as keys
      in feed_dict argument to session.run(), an error will be raised.
      Also, because some assert ops don't make it into the graph, the
      graph structure changes. A different graph can result in
      different per-op random seeds when they are not given
      explicitly (most often).
    * The following APIs are not longer experimental:
      tf.config.list_logical_devices, tf.config.list_physical_devices,
      tf.config.get_visible_devices, tf.config.set_visible_devices,
      tf.config.get_logical_device_configuration,
      tf.config.set_logical_device_configuration.
    * tf.config.experimentalVirtualDeviceConfiguration has been
      renamed to tf.config.LogicalDeviceConfiguration.
    * tf.config.experimental_list_devices has been removed,
      please use tf.config.list_logical_devices.
  - renamed the project to tensorflow2 so that the original tensorflow
    v1 API compatible release can stay in factory. Following
    changes were made to achive this:
    * added tensorflow-v2.1.0.tar.gz
    * added tensforflow2.spec
    * added tensforflow2.changes
    * removed tensorflow-v1.13.2.tar.gz
    * removed tensorflow.spec
    * removed tensorflow.chnages
  - following source files had to be updated
    * updated abseil-cpp.tar.gz
    * updated bazel-toolchains.tar.gz
    * updated eigen.tar.gz
    * updated gemmlowp.zip
    * updated license.rst.txt
    * updated rules_closure.tar.gz
  - following new souces had to be updated
    * added aws-sdk-cpp-1.5.8.tar.gz
    * added bazel-skylib.0.8.0.tar.gz
    * added fft2d.tgz
    * added rules_cc.zip
  - for the following souces the system libraries are now ues
    * removed aws-sdk-cpp-1.3.15.tar.gz
    * removed double_conversion.zip
    * removed file unicode-org-icu.tar.gz
    * removed file 816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz
  - these patches were removed
    * removed file support-new-bazel.patch
    * removed file tensorflow-make_aws_sdk_work_on_aarch64.patch
    * removed file tensorflow-fix_lite.patch
    * removed file remove-keras.patch
    * removed file grpc-namespace-corrections.patch
  - these new patches were added
    * added fix-lite.patch
    * added removed-docker-tools.patch
    * added right-json-location.patch

Files

/usr/include/tensorflow/core/public
/usr/include/tensorflow/core/public/version.h
/usr/include/tensorflow/lite
/usr/include/tensorflow/lite/allocation.h
/usr/include/tensorflow/lite/arena_planner.h
/usr/include/tensorflow/lite/builtin_op_data.h
/usr/include/tensorflow/lite/builtin_ops.h
/usr/include/tensorflow/lite/c
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/usr/include/tensorflow/lite/c/c_api.h
/usr/include/tensorflow/lite/c/c_api_experimental.h
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/usr/include/tensorflow/lite/c/c_api_types.h
/usr/include/tensorflow/lite/c/common.h
/usr/include/tensorflow/lite/c/common_internal.h
/usr/include/tensorflow/lite/context.h
/usr/include/tensorflow/lite/context_util.h
/usr/include/tensorflow/lite/core
/usr/include/tensorflow/lite/core/api
/usr/include/tensorflow/lite/core/api/error_reporter.h
/usr/include/tensorflow/lite/core/api/flatbuffer_conversions.h
/usr/include/tensorflow/lite/core/api/op_resolver.h
/usr/include/tensorflow/lite/core/api/op_resolver_internal.h
/usr/include/tensorflow/lite/core/api/profiler.h
/usr/include/tensorflow/lite/core/api/tensor_utils.h
/usr/include/tensorflow/lite/core/api/verifier.h
/usr/include/tensorflow/lite/core/macros.h
/usr/include/tensorflow/lite/core/shims
/usr/include/tensorflow/lite/core/shims/c
/usr/include/tensorflow/lite/core/shims/c/builtin_op_data.h
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/usr/include/tensorflow/lite/core/shims/c/c_api_experimental.h
/usr/include/tensorflow/lite/core/shims/c/common.h
/usr/include/tensorflow/lite/core/shims/c/experimental
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/usr/include/tensorflow/lite/core/shims/cc
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/usr/include/tensorflow/lite/delegates/coreml/builders/op_validator.h
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Fabrice Bellet, Sat Apr 27 23:55:11 2024