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| Name: tensorflow-lite-devel | Distribution: openSUSE Tumbleweed |
| Version: 2.10.0 | Vendor: openSUSE |
| Release: 3.3 | Build date: Sat Feb 10 00:48:29 2024 |
| Group: Development/Languages/Python | Build host: i01-armsrv2 |
| Size: 73315834 | Source RPM: tensorflow-lite-2.10.0-3.3.src.rpm |
| Packager: http://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.
Apache-2.0 AND BSD-2-Clause AND BSD-3-Clause AND MIT AND MPL-2.0
* 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
/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 /usr/include/tensorflow/lite/c/builtin_op_data.h /usr/include/tensorflow/lite/c/c_api.h /usr/include/tensorflow/lite/c/c_api_experimental.h /usr/include/tensorflow/lite/c/c_api_for_testing.h /usr/include/tensorflow/lite/c/c_api_internal.h /usr/include/tensorflow/lite/c/c_api_opaque.h /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 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/usr/include/tensorflow/lite/core/shims/c/experimental/acceleration/configuration/gpu_plugin.h /usr/include/tensorflow/lite/core/shims/c/experimental/acceleration/configuration/nnapi_plugin.h /usr/include/tensorflow/lite/core/shims/c/experimental/acceleration/configuration/xnnpack_plugin.h /usr/include/tensorflow/lite/core/shims/c/shims_test_util.h /usr/include/tensorflow/lite/core/shims/cc /usr/include/tensorflow/lite/core/shims/cc/create_op_resolver.h /usr/include/tensorflow/lite/core/shims/cc/experimental /usr/include/tensorflow/lite/core/shims/cc/experimental/acceleration /usr/include/tensorflow/lite/core/shims/cc/experimental/acceleration/configuration /usr/include/tensorflow/lite/core/shims/cc/experimental/acceleration/configuration/delegate_registry.h /usr/include/tensorflow/lite/core/shims/cc/interpreter.h /usr/include/tensorflow/lite/core/shims/cc/interpreter_builder.h /usr/include/tensorflow/lite/core/shims/cc/kernels /usr/include/tensorflow/lite/core/shims/cc/kernels/builtin_op_kernels.h /usr/include/tensorflow/lite/core/shims/cc/kernels/register.h /usr/include/tensorflow/lite/core/shims/cc/model.h /usr/include/tensorflow/lite/core/shims/cc/model_builder.h /usr/include/tensorflow/lite/core/shims/cc/shims_test_util.h /usr/include/tensorflow/lite/core/shims/cc/tools /usr/include/tensorflow/lite/core/shims/cc/tools/verifier.h /usr/include/tensorflow/lite/core/shims/cc/tools/verifier_internal.h /usr/include/tensorflow/lite/core/shims/jni /usr/include/tensorflow/lite/core/shims/jni/jni_utils.h /usr/include/tensorflow/lite/core/subgraph.h /usr/include/tensorflow/lite/create_op_resolver.h /usr/include/tensorflow/lite/delegates /usr/include/tensorflow/lite/delegates/coreml /usr/include/tensorflow/lite/delegates/coreml/builders /usr/include/tensorflow/lite/delegates/coreml/builders/activation_layer_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/add_op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/concatenation_op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/convolution_op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/dummy_op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/fully_connected_op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/hardswish_op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/mul_op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/op_factory.h /usr/include/tensorflow/lite/delegates/coreml/builders/op_validator.h /usr/include/tensorflow/lite/delegates/coreml/builders/pad_op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/pooling_layer_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/reshape_op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/resize_bilinear_op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/softmax_op_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/test_util.h /usr/include/tensorflow/lite/delegates/coreml/builders/threshold_layer_builder.h /usr/include/tensorflow/lite/delegates/coreml/builders/util.h /usr/include/tensorflow/lite/delegates/coreml/coreml_delegate.h /usr/include/tensorflow/lite/delegates/coreml/coreml_delegate_kernel.h /usr/include/tensorflow/lite/delegates/coreml/coreml_executor.h /usr/include/tensorflow/lite/delegates/delegate_test_util.h /usr/include/tensorflow/lite/delegates/external /usr/include/tensorflow/lite/delegates/external/external_delegate.h /usr/include/tensorflow/lite/delegates/flex /usr/include/tensorflow/lite/delegates/flex/allowlisted_flex_ops.h /usr/include/tensorflow/lite/delegates/flex/allowlisted_flex_ops_internal.h /usr/include/tensorflow/lite/delegates/flex/buffer_map.h /usr/include/tensorflow/lite/delegates/flex/buffer_map_util.h /usr/include/tensorflow/lite/delegates/flex/delegate.h /usr/include/tensorflow/lite/delegates/flex/delegate_data.h /usr/include/tensorflow/lite/delegates/flex/kernel.h /usr/include/tensorflow/lite/delegates/flex/subgraph_resource.h /usr/include/tensorflow/lite/delegates/flex/test_util.h /usr/include/tensorflow/lite/delegates/flex/util.h /usr/include/tensorflow/lite/delegates/gpu /usr/include/tensorflow/lite/delegates/gpu/api.h /usr/include/tensorflow/lite/delegates/gpu/cl /usr/include/tensorflow/lite/delegates/gpu/cl/api.h /usr/include/tensorflow/lite/delegates/gpu/cl/buffer.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_arguments.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_command_queue.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_context.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_device.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_errors.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_event.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_image_format.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_kernel.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_memory.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_operation.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_program.h /usr/include/tensorflow/lite/delegates/gpu/cl/cl_test.h /usr/include/tensorflow/lite/delegates/gpu/cl/compiled_program_cache_generated.h /usr/include/tensorflow/lite/delegates/gpu/cl/default /usr/include/tensorflow/lite/delegates/gpu/cl/default/qcom_wrapper.h /usr/include/tensorflow/lite/delegates/gpu/cl/egl_sync.h /usr/include/tensorflow/lite/delegates/gpu/cl/environment.h /usr/include/tensorflow/lite/delegates/gpu/cl/gl_interop.h /usr/include/tensorflow/lite/delegates/gpu/cl/gpu_api_delegate.h /usr/include/tensorflow/lite/delegates/gpu/cl/gpu_object.h /usr/include/tensorflow/lite/delegates/gpu/cl/inference_context.h /usr/include/tensorflow/lite/delegates/gpu/cl/kernels /usr/include/tensorflow/lite/delegates/gpu/cl/kernels/cl_test.h /usr/include/tensorflow/lite/delegates/gpu/cl/kernels/converter.h /usr/include/tensorflow/lite/delegates/gpu/cl/linear_storage.h /usr/include/tensorflow/lite/delegates/gpu/cl/opencl_wrapper.h /usr/include/tensorflow/lite/delegates/gpu/cl/program_cache.h /usr/include/tensorflow/lite/delegates/gpu/cl/recordable_queue.h /usr/include/tensorflow/lite/delegates/gpu/cl/recordable_queue_builder.h /usr/include/tensorflow/lite/delegates/gpu/cl/serialization_generated.h /usr/include/tensorflow/lite/delegates/gpu/cl/tensor.h /usr/include/tensorflow/lite/delegates/gpu/cl/tensor_type_util.h /usr/include/tensorflow/lite/delegates/gpu/cl/texture2d.h /usr/include/tensorflow/lite/delegates/gpu/cl/util.h /usr/include/tensorflow/lite/delegates/gpu/common /usr/include/tensorflow/lite/delegates/gpu/common/access_type.h /usr/include/tensorflow/lite/delegates/gpu/common/convert.h /usr/include/tensorflow/lite/delegates/gpu/common/custom_parsers.h /usr/include/tensorflow/lite/delegates/gpu/common/custom_transformations.h /usr/include/tensorflow/lite/delegates/gpu/common/data_type.h /usr/include/tensorflow/lite/delegates/gpu/common/flops_util.h /usr/include/tensorflow/lite/delegates/gpu/common/google /usr/include/tensorflow/lite/delegates/gpu/common/google/status_macros.h /usr/include/tensorflow/lite/delegates/gpu/common/gpu_info.h /usr/include/tensorflow/lite/delegates/gpu/common/gpu_model.h /usr/include/tensorflow/lite/delegates/gpu/common/gpu_model_generated.h /usr/include/tensorflow/lite/delegates/gpu/common/kernel_info.h /usr/include/tensorflow/lite/delegates/gpu/common/lstm_parser.h /usr/include/tensorflow/lite/delegates/gpu/common/memory_management /usr/include/tensorflow/lite/delegates/gpu/common/memory_management.h /usr/include/tensorflow/lite/delegates/gpu/common/memory_management/equality_assignment.h /usr/include/tensorflow/lite/delegates/gpu/common/memory_management/greedy_by_breadth_assignment.h /usr/include/tensorflow/lite/delegates/gpu/common/memory_management/greedy_by_size_assignment.h /usr/include/tensorflow/lite/delegates/gpu/common/memory_management/greedy_in_order_assignment.h /usr/include/tensorflow/lite/delegates/gpu/common/memory_management/internal.h /usr/include/tensorflow/lite/delegates/gpu/common/memory_management/min_cost_flow_assignment.h /usr/include/tensorflow/lite/delegates/gpu/common/memory_management/naive_assignment.h /usr/include/tensorflow/lite/delegates/gpu/common/memory_management/types.h /usr/include/tensorflow/lite/delegates/gpu/common/model.h /usr/include/tensorflow/lite/delegates/gpu/common/model_builder.h /usr/include/tensorflow/lite/delegates/gpu/common/model_builder_helper.h /usr/include/tensorflow/lite/delegates/gpu/common/model_builder_internal.h /usr/include/tensorflow/lite/delegates/gpu/common/model_hints.h /usr/include/tensorflow/lite/delegates/gpu/common/model_transformer.h 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Fabrice Bellet, Sat Apr 27 00:31:59 2024