GitHub Release: tensorflow/tensorflow v2.22.0-rc0 (22.09.2026)

The TensorBoard dependency is no longer included by default. If you use tf.summary.* APIs or tf.keras.callbacks.TensorBoard, please install the tensorboard package separately (pip install tensorboard). Otherwise, TensorFlow will raise an ImportError.

In tensorflow/c/experimental/filesystem/filesystem_interface.h, removed TF_TransactionToken and associated APIs.

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  <CAVEATS REGARDING THE RELEASE (BUT NOT BREAKING CHANGES).>

  
  


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  <ADDING/BUMPING DEPENDENCIES SHOULD GO HERE>

  
  


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  <KNOWN LACK OF SUPPORT ON SOME PLATFORM, SHOULD GO HERE>

  
  


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  tf.lite

  
  
  
  
  • Adds support for QUI4 (Quantized Unsigned 4-bit) in Dequantize operator.
  
  
  • Adds support for FP16 and BF16 in Unpack operator.
  
  
  • Adds support for FP16 in Transpose and DynamicUpdateSlice operator.
  
  
  • Transpose now supports up to 8D tensors.
  
  
  • Adds support for FLOAT8_E4M3FN and FLOAT8_E5M2 data types.
  
  
  
  


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  BatchFunction Operator

  
  
  
  
  • Adds the num_warmup_batch_threads op attribute to support a separate thread pool for processing warmup requests.
  
  
  • Adds the per_criticality_batch_timeout_micros op attribute to support different batch timeouts for different criticality levels.
  
  
  
  


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  TensorFlow API

  
  
  
  
  • Exports __new__ in public API golden files for subclasses of tuple (like tf.io.FixedLenFeature) to fix false positives during static type checking.>
  
  
  
  


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  tf.data

  
  
  
  
  • Fixes a bug in tf.data.Dataset.scan where the shape of the state returned by scan_func was not strictly validated against the initial state.
  
  
  
  


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  tf.image.adjust_contrast

  
  
  
  
  • Registers the missing Python gradient for the AdjustContrastv2 op, so
    
    tf.image.adjust_contrast can now be differentiated with
    
    GradientTape. Fixes
    
    #126083.
  
  
  
  


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  tf.nn.softsign

  
  
  
  
  • Fixes second-order gradients of tf.nn.softsign. Differentiating twice
    
    previously failed with a lookup error because the SoftsignGrad
    
    backward op had no registered Python gradient.
  
  
  
  


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  tf.math.reciprocal

  
  
  
  
  • Constrains the XLA registration of Reciprocal and Inv to the types
    
    that have a device kernel, so jit_compile=True no longer silently
    
    accepts the integer inputs that eager execution and autoclustering
    
    reject. Fixes
    
    #126414.
  
  
  
  


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  tf.experimental.numpy

  
  
  
  
  • tf.experimental.numpy.isclose and tf.experimental.numpy.allclose now
    
    apply rtol and atol to integer inputs, matching NumPy, instead of
    
    comparing with pure equality. The comparison stays in integer
    
    arithmetic; combining integer inputs with floating-point tolerances
    
    (including the defaults) now raises an error unless automatic type
    
    promotion is enabled, which keeps the cost of promotion opt-in.
  
  
  
  


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  oneDNN (MKL) convolution and transpose kernels

  
  
  
  
  • Raises InvalidArgumentError instead of aborting the process for a
    
    rank-mismatched Conv3DBackpropFilterV2 input and for
    
    ConjugateTranspose on a scalar. Fixes
    
    #118340 and
    
    #118345.
  
  
  
  


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  tf.image.non_max_suppression

  
  
  
  
  • Fixes a segmentation fault under jit_compile=True when boxes and
    
    scores are empty. The XLA lowering now returns an empty selection,
    
    matching the non-XLA kernels. Fixes
    
    #117245.
  
  
  
  


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  <SIMILAR TO ABOVE SECTION, BUT FOR OTHER IMPORTANT CHANGES / BUG FIXES>

  
  


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  <IF A CHANGE CLOSES A GITHUB ISSUE, IT SHOULD BE DOCUMENTED HERE>

  
  


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This release contains contributions from many people at Google, as well as:

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