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I_kwDOArmXAs6B1t9i
| 63,391 |
conv2d op produces output without nan with nan inputs
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[
"@pzy2000 If the lambda function or dropout layer is causing the NaNs, can we consider removing them and potentially adding alternative functionalities elsewhere in the network ? The pre-processing data also needs to be verified once. Please use the latest stable TF version 2.16 and let us know?\r\nThank you!",
"> @pzy2000 If the lambda function or dropout layer is causing the NaNs, can we consider removing them and potentially adding alternative functionalities elsewhere in the network? The pre-processing data also needs to be verified once. Please use the latest stable TF version 2.16 and let us know? Thank you!\r\n\r\nThanks for your answer! The reason why we use lambda function or dropout layer that is causing the NaNs is because we hope to detect framework defects like this with this method, since in my comprehension, given a conv2d ops, if input is NaNs, then output should also be NaNs. If my theory is mistaken, you are welcome to point it out! I will try it in latest version in a few hrs.",
"@pzy2000 Could you please let us know if you have tried it in the latest TF? I tried it but faced a different issue, so could you please share all the dependencies for replicating?\r\nThank you!",
"I just tried on latest tf, and it produced the following issue, of which I suppose was caused by inconsistent tf version:\r\n\r\n\r\nTraceback (most recent call last):\r\n File \"/root/zmx/COMET-master/issue/issue_tf_conv2d.py\", line 218, in <module>\r\n model = keras.models.load_model(\r\n File \"/root/miniconda3/envs/tf_latest/lib/python3.9/site-packages/keras/src/saving/saving_api.py\", line 183, in load_model\r\n return legacy_h5_format.load_model_from_hdf5(filepath)\r\n File \"/root/miniconda3/envs/tf_latest/lib/python3.9/site-packages/keras/src/legacy/saving/legacy_h5_format.py\", line 133, in load_model_from_hdf5\r\n model = saving_utils.model_from_config(\r\n File \"/root/miniconda3/envs/tf_latest/lib/python3.9/site-packages/keras/src/legacy/saving/saving_utils.py\", line 85, in model_from_config\r\n return serialization.deserialize_keras_object(\r\n File \"/root/miniconda3/envs/tf_latest/lib/python3.9/site-packages/keras/src/legacy/saving/serialization.py\", line 495, in deserialize_keras_object\r\n deserialized_obj = cls.from_config(\r\n File \"/root/miniconda3/envs/tf_latest/lib/python3.9/site-packages/keras/src/models/model.py\", line 528, in from_config\r\n return functional_from_config(\r\n File \"/root/miniconda3/envs/tf_latest/lib/python3.9/site-packages/keras/src/models/functional.py\", line 509, in functional_from_config\r\n process_layer(layer_data)\r\n File \"/root/miniconda3/envs/tf_latest/lib/python3.9/site-packages/keras/src/models/functional.py\", line 489, in process_layer\r\n layer = saving_utils.model_from_config(\r\n File \"/root/miniconda3/envs/tf_latest/lib/python3.9/site-packages/keras/src/legacy/saving/saving_utils.py\", line 85, in model_from_config\r\n return serialization.deserialize_keras_object(\r\n File \"/root/miniconda3/envs/tf_latest/lib/python3.9/site-packages/keras/src/legacy/saving/serialization.py\", line 495, in deserialize_keras_object\r\n deserialized_obj = cls.from_config(\r\n File \"/root/miniconda3/envs/tf_latest/lib/python3.9/site-packages/keras/src/layers/core/lambda_layer.py\", line 193, in from_config\r\n fn = python_utils.func_load(\r\n File \"/root/miniconda3/envs/tf_latest/lib/python3.9/site-packages/keras/src/utils/python_utils.py\", line 83, in func_load\r\n code = marshal.loads(raw_code)\r\nValueError: bad marshal data (unknown type code)\r\n",
"@pzy2000 The error occurs when the keras.models.load_model function attempts to deserialize a custom Lambda layer configuration saved in the model. It might be occurring for keras model loading. Please refer Keras documentation on saving and loading models [here](https://www.tensorflow.org/tutorials/keras/save_and_load ). \r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63391\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63391\">No</a>\n"
] | 2024-03-11T06:39:45 | 2024-04-11T01:48:13 | 2024-04-11T01:48:08 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.10.0
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04.4
### Mobile device
_No response_
### Python version
3.7.6
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.2
### GPU model and memory
_No response_
### Current behavior?
we expect layer 5 output to be also nan, since layer 5 input is nan
[lenet5-fashion-mnist_origin-SpecialI5-LMerg34.zip](https://github.com/tensorflow/tensorflow/files/14554559/lenet5-fashion-mnist_origin-SpecialI5-LMerg34.zip)
### Standalone code to reproduce the issue
```shell
import keras
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
def _init_input(input_shape):
input_shape = list(input_shape)
input_shape[0] = 10
input_shape = tuple(input_shape)
input = np.random.rand(*input_shape)
return input
class CustomCastLayer(keras.layers.Layer):
def __init__(self, target_dtype, **kwargs):
self.target_dtype = target_dtype
super().__init__(**kwargs)
def build(self, input_shape):
"""
Do Nothing
"""
super().build(input_shape)
def call(self, inputs, **kwargs):
output = tf.cast(inputs, self.target_dtype)
return output
def get_config(self):
config = super().get_config()
config.update({"target_dtype": self.target_dtype})
return config
def compute_output_shape(self, input_shape):
"""
Do Nothing
"""
return input_shape
class CustomPadLayer(keras.layers.Layer):
def __init__(self, padding=[[0, 0]], constant_values=0, **kwargs):
self.padding = padding # add [0,0] to padding so we will not change the shape of batch dimension
self.constant_values = constant_values
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
output = tf.pad(inputs, paddings=tf.constant([[0, 0]] + self.padding), mode="CONSTANT",
constant_values=self.constant_values)
return output
def get_config(self):
config = super().get_config()
config.update({"padding": self.padding, "constant_values": self.constant_values})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is (None, N, W, C), the `paddings` is [[a, b], [c, d]].
The output_shape is (None, N+a+b, W+c+d, C).
"""
input_shape_list = list(input_shape)
padding = [[0, 0]] + self.padding
assert len(input_shape_list) == len(padding) # Two dimensions should match.
output_shape = [None]
for i, pad in zip(input_shape_list[1:], padding[1:]):
output_shape.append(i + pad[0] + pad[1])
return tuple(output_shape)
class CustomCropLayer(keras.layers.Layer):
def __init__(self, cropping, **kwargs):
self.cropping = cropping
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
input_shape = inputs.shape.as_list()
indices = [slice(None)]
cropping = [[0, 0]] + self.cropping # add [0,0] to padding so we will not change the shape of batch dimension
for shape, crop in zip(input_shape[1:], cropping[1:]):
indices.append(slice(0 + crop[0], shape - crop[1]))
return inputs[indices]
def get_config(self):
config = super().get_config()
config.update({"cropping": self.cropping})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is (None, N, W, C), the `cropping` is [[a, b], [c, d]].
The output_shape is (None, N-a-b, W-c-d, C).
"""
input_shape_list = list(input_shape)
cropping = [[0, 0]] + self.cropping
assert len(input_shape_list) == len(cropping) # Two dimensions should match.
output_shape = [None]
for i, crop in zip(input_shape[1:], cropping[1:]):
output_shape.append(i - crop[0] - crop[1])
return tuple(output_shape)
class CustomExpandLayer(keras.layers.Layer):
def __init__(self, axis=1, **kwargs):
self.axis = axis
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
return tf.expand_dims(inputs, self.axis)
def get_config(self):
config = super().get_config()
config.update({"axis": self.axis})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is [None, N, W, C]:
axis=0:
output_shape: [1, None, N, W, C]
axis=1 (default):
output_shape: [None, 1, N, W, C]
axis=2:
output_shape: [None, N, 1, W, C]
axis=3:
output_shape: [None, N, W, 1, C]
axis=4:
output_shape: [None, N, W, C, 1]
axis=5:
raise Exception
"""
input_shape_list = list(input_shape)
if self.axis > len(input_shape_list):
raise ValueError(f"axis {self.axis} should be smaller than input_shape + 1: {len(input_shape_list) + 1}")
output_shape = input_shape_list[0:self.axis] + [1] + input_shape_list[self.axis:]
return tuple(output_shape) # we should use tuple!!! not list !!!
class CustomDropDimLayer(keras.layers.Layer):
def __init__(self, axis=1, **kwargs):
self.axis = axis
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
"""
Something magic to automatically generate indices for array slicing.
To determine a specific axis, we can use slice(None) to replace `:`
"""
dim = len(inputs.shape)
if self.axis > dim - 1 or self.axis < 1:
raise ValueError(f"axis: {self.axis} should be within the range: [1, {dim - 1}] for {dim}D tensor")
indices = [slice(None) for i in range(dim)]
indices[self.axis] = 0
return inputs[indices]
def get_config(self):
config = super().get_config()
config.update({"axis": self.axis})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is [None, N, W, C]:
axis=0: # Although it is feasible, we don't allow this to happen
Raise Exception
axis=1 (default):
output_shape: [None, W, C]
axis=2:
output_shape: [None, N, C]
axis=3:
output_shape: [None, N, W]
axis=4:
Raise Exception
"""
input_shape_list = list(input_shape)
output_shape = input_shape_list[0:self.axis] + input_shape_list[self.axis + 1:]
return tuple(output_shape)
def custom_objects(mode="custom"):
def no_activation(x):
return x
def leakyrelu(x):
import keras.backend as K
return K.relu(x, alpha=0.01)
# objects = {}
objects = {'no_activation': no_activation, 'leakyrelu': leakyrelu}
if mode == "custom":
objects['CustomPadLayer'] = CustomPadLayer
objects['CustomCropLayer'] = CustomCropLayer
objects['CustomDropDimLayer'] = CustomDropDimLayer
objects['CustomExpandLayer'] = CustomExpandLayer
objects['CustomCastLayer'] = CustomCastLayer
return objects
# 加载预训练模型
model = keras.models.load_model(
'lenet5-fashion-mnist_origin-SpecialI5-LMerg34.h5',
custom_objects=custom_objects())
input_layer = model.input
layer_outputs = [layer.output for layer in model.layers]
print(layer_outputs)
print("===================================")
new_model = Model(inputs=input_layer, outputs=layer_outputs)
# 创建一个具有特定形状的随机tensor，例如形状为(1, 3, 32, 32)
with tf.device('cpu'):
input_tensor = _init_input(model.input_shape)
all_layer_outputs = new_model.predict(input_tensor)
print("===================================")
for i, layer_output in enumerate(all_layer_outputs):
print(f"Name of layer {i}: {model.layers[i].name}")
print(f"Output if nan of layer {i}: {np.isnan(layer_output).any()}")
print()
```
### Relevant log output
```shell
Name of layer 0: conv2d_1_input
Output if nan of layer 0: False
Name of layer 1: conv2d_1_copy_SpecialI_copy_LMerg
Output if nan of layer 1: False
Name of layer 2: max_pooling2d_1_copy_SpecialI_copy_LMerg
Output if nan of layer 2: False
Name of layer 3: lambda_copy_LMerg
Output if nan of layer 3: True
Name of layer 4: dropout_1_copy_SpecialI_copy_LMerg
Output if nan of layer 4: True
Name of layer 5: conv2d_2_copy_SpecialI_copy_LMerg
Output if nan of layer 5: False
Name of layer 6: max_pooling2d_2_copy_SpecialI_copy_LMerg
Output if nan of layer 6: False
Name of layer 7: dropout_2_copy_SpecialI_copy_LMerg_merge1
Output if nan of layer 7: False
Name of layer 8: subtract_copy_ML
Output if nan of layer 8: False
Name of layer 9: flatten_1_copy_SpecialI_copy_LMerg
Output if nan of layer 9: False
Name of layer 10: dense_1_copy_SpecialI_copy_LMerg
Output if nan of layer 10: False
Name of layer 11: dense_2_copy_SpecialI_copy_LMerg
Output if nan of layer 11: False
Name of layer 12: dense_3_copy_SpecialI_copy_LMerg
Output if nan of layer 12: False
Name of layer 13: dense_insert
Output if nan of layer 13: False
```
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PR_kwDOArmXAs5pMjbv
| 63,390 |
[xla] Extend collective-permute decomposer to also make decision for
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[xla] Extend collective-permute decomposer to also make decision for
Send-Recv pipeling and record the decision with frontend attributes.
We first use a simple heuristics to decide on the decomposition of which
CollectivePermute operations will be pipelined. We will only pipeline
CollectivePermute that sends loop input data, and pick the first
pipelineable CollectivePermute for pipelining. Then, if there is another
pipelineable CollectivePermute that forms a cycle with the to-be-pipelined
CollectivePermute, we will pipeline both CollectivePermute. Otherwise, we will
only pipeline one CollectivePermute.
Then, when we decompose CollectivePermute operations, we add a frontend
attribute to the Send/Recv operation to represent the pipelining decision.
Add tests.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62782 from DanielYang59:warn-shuffle 134fc245a3f6c9362b04ba8dff3b84e703401549
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PR_kwDOArmXAs5pMZ3G
| 63,389 |
Microoptmize the conditions in IsArrayType.
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Microoptmize the conditions in IsArrayType.
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PR_kwDOArmXAs5pMY4Q
| 63,388 |
Do not call Shape::is_static when unnecessary.
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Do not call Shape::is_static when unnecessary.
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PR_kwDOArmXAs5pMYtN
| 63,387 |
Eliminate unnecessary copies for HloSharding.
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Eliminate unnecessary copies for HloSharding.
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PR_kwDOArmXAs5pMJVd
| 63,386 |
Add Dynamic Range Quantized op support for `op_stat_pass.cc`.
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NONE
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Add Dynamic Range Quantized op support for `op_stat_pass.cc`.
- Cleanup header imports as well.
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PR_kwDOArmXAs5pL_SQ
| 63,385 |
Add check conditions in `quantization_driver_test.cc`.
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Add check conditions in `quantization_driver_test.cc`.
- Adds more rigorous checks for desired states in intermediate testing stages.
- Renames and rewrites `IsEmpty` and `HasQuantParams` for clarity.
- Follows the recommendations in go/totw/135.
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I_kwDOArmXAs6B0CDv
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2.16.1 libtensorflow binary
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[
"CC: @MichaelHudgins @Venkat6871 @learning-to-play \r\n\r\nIs this because the libternsorflow build jobs have been discontinued?",
"Additionally, the [libtensorflow nightly archive](https://storage.googleapis.com/libtensorflow-nightly?prefix=latest/libtensorflow-cpu-) also has not been updated for about sixty days. And a bit longer for the [protos archive](https://storage.googleapis.com/libtensorflow-nightly?prefix=latest/libtensorflow_proto) and a few other artifacts",
"These ones have not been discontinued at this time, it looks like an issue with some internal modifications not moving the built artifacts from a staging location to the previously expected final gcs bucket. ",
"Until documentation is corrected you can find it at \r\nhttps://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-gpu-linux-x86_64.tar.gz\r\nhttps://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-cpu-linux-x86_64.tar.gz",
"Hi **@yutkin** ,\r\nTensorFlow 2.16.1 introduced a new installation method for Linux that allows installing the necessary NVIDIA CUDA libraries through pip. This means you can use pip install tensorflow[gpu] (assuming you have the NVIDIA driver already installed) to get TensorFlow with GPU support, even if there's no separate libtensorflow binary.\r\n\r\nThank you!",
"Hi @Venkat6871 , this is not an issue with a pip installation but rather one from the libtensorflow libraries not being in the expected locaiton. ",
"@MichaelHudgins , I can't find the Windows libtensorflow under the new location, I've tried this, anything it wrong with that URL?\r\n\r\n[https://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-cpu-windows-x86_64-2.16.1.zip](https://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-cpu-windows-x86_64-2.16.1.zip)\r\n\r\nCan't find it in the previous location neither, last one is 2.15.0",
"List the content of the bucket, looks like Windows is nowhere to be seen. Has the libtensorflow build for Windows been deprecated? (hopefully not...)\r\n```\r\ngcloud storage ls gs://tensorflow/versions/2.16.1\r\ngs://tensorflow/versions/2.16.1/libtensorflow-cpu-darwin-arm64.tar.gz\r\ngs://tensorflow/versions/2.16.1/libtensorflow-cpu-darwin-x86_64.tar.gz\r\ngs://tensorflow/versions/2.16.1/libtensorflow-cpu-linux-x86_64.tar.gz\r\ngs://tensorflow/versions/2.16.1/libtensorflow-gpu-linux-x86_64.tar.gz\r\ngs://tensorflow/versions/2.16.1/libtensorflow_jni-cpu-darwin-arm64.tar.gz\r\ngs://tensorflow/versions/2.16.1/libtensorflow_jni-cpu-darwin-x86_64.tar.gz\r\ngs://tensorflow/versions/2.16.1/libtensorflow_jni-cpu-linux-x86_64.tar.gz\r\ngs://tensorflow/versions/2.16.1/libtensorflow_jni-gpu-linux-x86_64.tar.gz\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp310-cp310-macosx_10_15_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp310-cp310-macosx_12_0_arm64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp311-cp311-macosx_10_15_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp311-cp311-macosx_12_0_arm64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp312-cp312-macosx_10_15_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp312-cp312-macosx_12_0_arm64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp39-cp39-macosx_10_15_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp39-cp39-macosx_12_0_arm64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow-2.16.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow_cpu-2.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow_cpu-2.16.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow_cpu-2.16.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow_cpu-2.16.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow_tpu-2.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow_tpu-2.16.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow_tpu-2.16.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\ngs://tensorflow/versions/2.16.1/tensorflow_tpu-2.16.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\n```",
"This is blocking the TensorFlow Rust release.",
"> This is blocking the TensorFlow Rust release.\r\n\r\nSame for TensorFlow Java",
"Thanks for creating this issue. It looks like the Windows CI build uploaded to the incorrect location. I have moved it to be under `gs://tensorflow/versions/2.16.1`. \r\n\r\nhttps://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow-cpu-windows-x86_64.zip\r\nhttps://storage.googleapis.com/tensorflow/versions/2.16.1/libtensorflow_jni-cpu-windows-x86_64.zip",
"@nitins17 what about the GPU version? Thanks\r\n\r\nEDIT: I've just acknowledged that that must be a conscious decision to drop the support for GPU on Windows without relying on the Linux subsystem... such a bummer; I needed the DLL with GPU support as a dependency of another software.\r\nMy only option at this point is downgrading the CUDA version.",
"When is this expected to be resolved?",
"> When is this expected to be resolved?\r\n\r\nMy conservative guess would be by end of next week. The change to update the links on the website is in progress internally, just waiting on the final approval. ",
"> > When is this expected to be resolved?\r\n> \r\n> My conservative guess would be by end of next week. The change to update the links on the website is in progress internally, just waiting on the final approval.\r\n\r\nHi! Are there any updates? ",
"Hmm, the [commit has been merged](https://github.com/tensorflow/docs/commit/29fd0f4fe59582b65ec33102385189ea3a9ba9c5) but I'm not sure why it does not show up on the website. @MarkDaoust would you know?\r\n\r\n@yutkin In the meantime, you can use the preview in https://github.com/tensorflow/docs/blob/master/site/en/install/lang_c.ipynb to get the links to the latest archives. ",
"The docs still point to the 2.15 version."
] | 2024-03-10T20:56:00 | 2024-05-20T08:08:19 | null |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
Linux
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
Yes
### Current behavior?
Hi!
Tensorflow 2.16.1 has been [released](https://github.com/tensorflow/tensorflow/releases/tag/v2.16.1) recently. However, the latest archive with the `libtensorflow` on the official website [is still 2.15](https://www.tensorflow.org/install/lang_c). Where can I get the latest 2.16.1 `libtensorflow` with GPU support for Linux?
### Standalone code to reproduce the issue
```shell
-
```
### Relevant log output
_No response_
|
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Make function loading more concurrent with `TF_ENABLE_EAGER_CLIENT_STREAMING_ENQUEUE` set to `false`
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Make function loading more concurrent with `TF_ENABLE_EAGER_CLIENT_STREAMING_ENQUEUE` set to `false`
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Testing a temporary code change.
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Testing a temporary code change.
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[XLA:Python] Port py_values to nanobind.
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tf.tensor_scatter_nd_add: Aborted (core dumped)
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[
"@nonenone12135,\r\nCould you please confirm whether you are facing this issue/error only for the specific input or for the every input which you are passing through. I tested with the different input it was executed without crashing. Kindly find the [gist](https://colab.research.google.com/gist/tilakrayal/1aa2c2b0310d7fef130a2f7e0978125c/untitled1751.ipynb).\r\n\r\nArgs:\r\nindices--\tA Tensor. Must be one of the following types: int32, int64. Index tensor.\r\nupdates-- A Tensor. Must have the same type as tensor. Updates to scatter into output.\r\n\r\nThank you!",
"only for the specific input:\r\n```\r\ninput_tensor = tf.zeros([15, 15, 15])\r\nindices = tf.constant([[[0, 0, 0], [1, 1, 1]], [[2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5]], [[6, 6, 6], [7, 7, 7]], [[8, 8, 8], [9, 9, 9]], [[10, 10, 10], [11, 11, 11]], [[12, 12, 12], [13, 13, 13]], [[14, 14, 14], [0, 0, 0]], [[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]], [[7, 7, 7], [8, 8, 8]], [[9, 9, 9], [10, 10, 10]], [[11, 11, 11], [12, 12, 12]], [[13, 13, 13], [14, 14, 14]]])\r\nupdates = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0])\r\n```\r\n\r\nI tried the two examples you provided, and they worked fine."
] | 2024-03-10T15:00:49 | 2024-05-10T13:44:27 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.tensor_scatter_nd_add` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# Generate input data
input_tensor = tf.zeros([15, 15, 15])
indices = tf.constant([[[0, 0, 0], [1, 1, 1]], [[2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5]], [[6, 6, 6], [7, 7, 7]], [[8, 8, 8], [9, 9, 9]], [[10, 10, 10], [11, 11, 11]], [[12, 12, 12], [13, 13, 13]], [[14, 14, 14], [0, 0, 0]], [[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]], [[7, 7, 7], [8, 8, 8]], [[9, 9, 9], [10, 10, 10]], [[11, 11, 11], [12, 12, 12]], [[13, 13, 13], [14, 14, 14]]])
updates = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0]) # Cast updates to float
# Invoke tf.tensor_scatter_nd_add
result = tf.tensor_scatter_nd_add(input_tensor, indices, updates)
# Print the result
print(result)
```
### Relevant log output
```shell
2024-03-10 14:59:51.853766: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
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tf.raw_ops.UnicodeEncode: Segmentation fault (core dumped)
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[
"Hi @nonenone12135 ,\r\n\r\nThanks for reporting. Replicated the reported issue. I am unsure why this check below is not worked.\r\nhttps://github.com/tensorflow/tensorflow/blob/6f64ad5d767a034df45a5eaab8b36fd688cd1217/tensorflow/core/ops/string_ops.cc#L317\r\n\r\nWill check and come back. Thanks!",
"@vraebfdsb,\r\nI tried to execute the mentioned code on tf-nightly and observed that the UnicodeEncode is executed without the Segmentation fault and the output error is as expected. Also the PR raised for the same has been merged. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/85e86643dc41cb3e81a091c566f33b31/untitled1952.ipynb).\r\n\r\nhttps://github.com/tensorflow/tensorflow/pull/63398\r\nNote: With tensorflow 2.15 and 2.16, the same Ops was failing with the Segmentation fault.\r\nThank you!"
] | 2024-03-10T14:59:08 | 2024-06-12T10:35:17 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.UnicodeEncode` encounters "Segmentation fault (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# Generate input data
input_values = tf.constant([72, 101, 108, 108, 111, 32, 87, 111, 114, 108, 100]) # Unicode codepoints for "Hello World"
input_splits = tf.constant([[0, 5, 11]]) # Split indices for the input_values with two dimensions
output_encoding = "UTF-8"
# Invoke tf.raw_ops.unicode_encode
output = tf.raw_ops.UnicodeEncode(input_values=input_values, input_splits=input_splits, output_encoding=output_encoding)
# Print the output
print(output)
```
### Relevant log output
```shell
Segmentation fault (core dumped)
```
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I_kwDOArmXAs6BzfJR
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tf.raw_ops.TensorScatterSub: Aborted (core dumped)
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[
"@sachinprasadhs I was able to replicate the error reported [here](https://colab.research.google.com/gist/sushreebarsa/7effb8658df4874391c707a1b4cdb35c/63378.ipynb). Thank you!"
] | 2024-03-10T14:57:36 | 2024-03-13T09:35:46 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.TensorScatterSub` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# Generate input data
tensor = tf.constant([1, 2, 3, 4, 5])
indices = tf.constant([[[1], [3]], [[0], [2]]]) # Nested structure for indices
updates = tf.constant([10, 20])
# Invoke tf.raw_ops.TensorScatterSub
result = tf.raw_ops.TensorScatterSub(tensor=tensor, indices=indices, updates=updates)
# Print the result
print(result)
```
### Relevant log output
```shell
2024-03-10 14:55:41.958738: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
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I_kwDOArmXAs6Bze6o
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tf.raw_ops.SparseConcat: Overflow bug
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[
"Hi **@nonenone12135** ,\r\n\r\nThe error message indicates an overflow from a large input shape. The issue lies in the shape2 variable. You have set the second dimension of shape2 to -1, which is a negative value. This negative value is interpreted as a mutable dimension, meaning that the size of that dimension can change. However, the SparseConcat operation requires all input shapes to be fully defined and non-mutable.\r\n\r\nTo fix this issue, you need to set the second dimension of shape2 to a positive value. In this case, since the second dimension represents the number of columns in the sparse matrix, you can set it to the same value as shape1, which is 4.\r\n```\r\nshape2 = tf.constant([3, 4], dtype=tf.int64) # Fixed shape\r\n```\r\n\r\nI am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/8a92282d3a8ebcf24aec3d418b22d908/63377_2-15-nightly-v.ipynb) here for your reference.\r\n\r\nThank you!\r\n",
"Thank you for your response. So, is this error message expected behavior rather than a bug?",
"Hi **@vraebfdsb** ,\r\n- Looks like it is working as intended, Meanwhile i will check with developer team on the same issue and will update the same.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63377\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63377\">No</a>\n"
] | 2024-03-10T14:55:13 | 2024-06-07T01:51:11 | 2024-06-07T01:51:08 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.SparseConcat` encounters overflow bug.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# Generate input data
indices1 = tf.constant([[0, 0], [1, 2]], dtype=tf.int64)
values1 = tf.constant([1, 2], dtype=tf.float32)
shape1 = tf.constant([3, 4], dtype=tf.int64)
indices2 = tf.constant([[0, 1], [2, 3]], dtype=tf.int64)
values2 = tf.constant([3, 4], dtype=tf.float32)
shape2 = tf.constant([-1, 4], dtype=tf.int64) # Mutated shape with the negative bit set
# Invoke tf.raw_ops.SparseConcat
concatenated_sparse = tf.raw_ops.SparseConcat(
indices=[indices1, indices2],
values=[values1, values2],
shapes=[shape1, shape2],
concat_dim=0
)
print(concatenated_sparse)
```
### Relevant log output
```shell
tensorflow.python.framework.errors_impl.InternalError: {{function_node __wrapped__SparseConcat_N_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Encountered overflow from large input shape. [Op:SparseConcat] name:
```
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I_kwDOArmXAs6Bzegi
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tf.raw_ops.FusedPadConv2D: Aborted (core dumped)
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[
"@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow [v2.15](https://colab.research.google.com/gist/tilakrayal/c510f241f5ad6df92a119baa25e37609/untitled1753.ipynb) and the tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/3d8e36b937c66dd8f3056b4f6d64b1af/untitled1754.ipynb). "
] | 2024-03-10T14:51:07 | 2024-03-13T09:57:51 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.FusedPadConv2D` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# Generate input data
input_data = tf.random.normal([3, 10, 10])
# Define paddings
paddings = tf.constant([[0, 0], [1, 1], [1, 1]])
# Define filter
filter = tf.random.normal([3, 3, 3, 16])
# Define mode
mode = "REFLECT" # Change mode to "REFLECT" or "SYMMETRIC"
# Define strides
strides = [1, 1, 1, 1]
# Define padding
padding = "VALID"
# Invoke tf.raw_ops.FusedPadConv2D
output = tf.raw_ops.FusedPadConv2D(input=input_data, paddings=paddings, filter=filter, mode=mode, strides=strides, padding=padding)
print(output)
```
### Relevant log output
```shell
2024-03-10 14:49:28.555826: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (3 vs. 3)
Aborted (core dumped)
```
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I_kwDOArmXAs6BzePk
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tf.tensor_scatter_nd_update: Aborted (core dumped)
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[
"Hi @nonenone12135 ,\r\n\r\nThis should probably addressed by this PR #63725 .",
"@vraebfdsb ,\r\nThe PR https://github.com/tensorflow/tensorflow/pull/63725 which was raised was closed and mentioned that \r\n``` python\r\nThe assert that should fail is the last one, which has an equality assertion on the sizes of updates.shape == batch_shape + inner_shape\r\n```\r\n\r\nhttps://github.com/tensorflow/tensorflow/pull/63725\r\n\r\nCould you please take a look and provide the context to fulfill and get the PR merged. Thank you!"
] | 2024-03-10T14:48:19 | 2024-06-10T11:00:22 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.tensor_scatter_nd_update` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# Generate input data
input_tensor = tf.zeros([2, 2, 2]) # A tensor that contains other tensors, creating a nested structure
indices = tf.constant([[[0, 0, 0], [1, 1, 1]], [[1, 0, 1], [0, 1, 0]]])
updates = tf.constant([1, 2], dtype=tf.float32) # Cast updates to float
# Invoke tf.tensor_scatter_nd_update
result = tf.tensor_scatter_nd_update(input_tensor, indices, updates)
# Print the result
print(result)
```
### Relevant log output
```shell
2024-03-10 14:36:43.315650: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
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Automated Code Change
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"I finally fixed the privious questions(Now I have modifeid the title and report content) about incompatible version by using protobuf v3.21.9, which should be compitable with tf2.15.\r\n\r\nHowever I got some errors after build my code:\r\n```\r\n2024-03-11 01:24:42.588892: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2024-03-11 01:24:42.588959: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2024-03-11 01:24:42.589806: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n```\r\n\r\nI checked my cuda version is 12.2 and cudnn version is 8.9.7.\r\nAny ideas about this issue?",
"@doolachen The errors indicate a conflict between multiple cuDNN, cuFFT, and cuBLAS libraries during your TensorFlow build process. Could you try cleaning your TensorFlow build directory (bazel clean) and rebuilding it to ensure a fresh build with the updated environment.\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63366\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63366\">No</a>\n"
] | 2024-03-10T04:22:46 | 2024-03-29T01:47:01 | 2024-03-29T01:46:55 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.15.0
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
6.1.0
### GCC/compiler version
11.4.0
### CUDA/cuDNN version
12.2/8.9.7
### GPU model and memory
GTX 3090/24G
### Current behavior?
I first compiled TensorFlow using Bazel according to the official documentation, these are my operations:
`git clone https://github.com/tensorflow/tensorflow`
`cd tensorflow`
`git checkout r2.15`
`./configure `
and information is:
>
> You have bazel 6.1.0 installed.
> Please specify the location of python. [Default is /usr/bin/python3]:
>
>
> Found possible Python library paths:
> /usr/lib/python3/dist-packages
> /usr/local/lib/python3.10/dist-packages
> Please input the desired Python library path to use. Default is [/usr/lib/python3/dist-packages]
>
> Do you wish to build TensorFlow with ROCm support? [y/N]: n
> No ROCm support will be enabled for TensorFlow.
>
> Do you wish to build TensorFlow with CUDA support? [y/N]: y
> CUDA support will be enabled for TensorFlow.
>
> Do you wish to build TensorFlow with TensorRT support? [y/N]: n
> No TensorRT support will be enabled for TensorFlow.
>
> Found CUDA 12.2 in:
> /usr/local/cuda-12.2/targets/x86_64-linux/lib
> /usr/local/cuda-12.2/targets/x86_64-linux/include
> Found cuDNN 8 in:
> /usr/lib/x86_64-linux-gnu
> /usr/include
>
>
> Please specify a list of comma-separated CUDA compute capabilities you want to build with.
> You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Each capability can be specified as "x.y" or "compute_xy" to include both virtual and binary GPU code, or as "sm_xy" to only include the binary code.
> Please note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 8.6]:
>
>
> Do you want to use clang as CUDA compiler? [Y/n]: n
> nvcc will be used as CUDA compiler.
>
> Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
>
>
> Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -Wno-sign-compare]:
>
>
> Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
> Not configuring the WORKSPACE for Android builds.
>
> Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
> --config=mkl # Build with MKL support.
> --config=mkl_aarch64 # Build with oneDNN and Compute Library for the Arm Architecture (ACL).
> --config=monolithic # Config for mostly static monolithic build.
> --config=numa # Build with NUMA support.
> --config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.
> --config=v1 # Build with TensorFlow 1 API instead of TF 2 API.
> Preconfigured Bazel build configs to DISABLE default on features:
> --config=nogcp # Disable GCP support.
> --config=nonccl # Disable NVIDIA NCCL support.
> Configuration finished
check my cuda version using `nvcc -V`:
```
wangchen@wc:~/tfc++test$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Tue_Jun_13_19:16:58_PDT_2023
Cuda compilation tools, release 12.2, V12.2.91
Build cuda_12.2.r12.2/compiler.32965470_0
```
check my cudnn version using `cat /usr/include/x86_64-linux-gnu/cudnn_v*.h | grep CUDNN_MAJOR -A 2`:
```
wangchen@wc:~/tfc++test$ cat /usr/include/x86_64-linux-gnu/cudnn_v*.h | grep CUDNN_MAJOR -A 2
#define CUDNN_MAJOR 8
#define CUDNN_MINOR 9
#define CUDNN_PATCHLEVEL 7
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
/* cannot use constexpr here since this is a C-only file */
```
and I then compile with bazel:
`bazel build --config=cuda tensorflow:tensorflow_cc`
`bazel build tensorflow:install_headers`
There were no issues, I successfully compiled the header files and link libraries I wanted in the `bazel-bin` folder.
But when I try to compile a C++ sample:
```
#include "tensorflow/cc/client/client_session.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/tensor.h"
using namespace tensorflow;
using namespace tensorflow::ops;
int main() {
Scope root = Scope::NewRootScope();
// Matrix A = [3 2; -1 0]
auto A = Const(root, { {3.f, 2.f}, {-1.f, 0.f} });
// Vector b = [3 5]
auto b = Const(root, { {3.f, 5.f} });
// v = Ab^T
auto v = MatMul(root.WithOpName("v"), A, b, MatMul::TransposeB(true));
std::vector<Tensor> outputs;
ClientSession session(root);
// Run and fetch v
TF_CHECK_OK(session.Run({v}, &outputs));
// Expect outputs[0] == [19; -3]
LOG(INFO) << outputs[0].matrix<float>();
return 0;
}
```
command is
`g++ -std=c++17 -I/home/wangchen/tensorflow/bazel-bin/tensorflow/include -L/home/wangchen/tensorflow/bazel-bin/tensorflow/ tf_example.cpp -o tftest -ltensorflow_framework -ltensorflow_cc`
After successfully compile my code, I got an error when I tried to run it :
```
2024-03-11 01:24:42.588892: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-03-11 01:24:42.588959: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-03-11 01:24:42.589806: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
```
### Standalone code to reproduce the issue
```
#include "tensorflow/cc/client/client_session.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/tensor.h"
using namespace tensorflow;
using namespace tensorflow::ops;
int main() {
Scope root = Scope::NewRootScope();
// Matrix A = [3 2; -1 0]
auto A = Const(root, { {3.f, 2.f}, {-1.f, 0.f} });
// Vector b = [3 5]
auto b = Const(root, { {3.f, 5.f} });
// v = Ab^T
auto v = MatMul(root.WithOpName("v"), A, b, MatMul::TransposeB(true));
std::vector<Tensor> outputs;
ClientSession session(root);
// Run and fetch v
TF_CHECK_OK(session.Run({v}, &outputs));
// Expect outputs[0] == [19; -3]
LOG(INFO) << outputs[0].matrix<float>();
return 0;
}
```
### Relevant log output
```
2024-03-11 01:34:55.525804: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-03-11 01:34:55.525867: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-03-11 01:34:55.526735: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-03-11 01:34:55.597359: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-11 01:34:55.715017: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2256] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2024-03-11 01:34:55.791239: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-03-11 01:34:55.800049: I /home/wangchen/tfc++test/tf_example.cpp:21] 19
```
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[
"Some sample code\r\n```\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.layers import TextVectorization\r\n\r\ndata = [\r\n \"The sky is blue.\",\r\n \"Grass is green.\",\r\n]\r\n\r\n# Create vectorizer\r\ntext_dataset = tf.data.Dataset.from_tensor_slices(data)\r\nvectorizer = TextVectorization(\r\n max_tokens=1000, output_mode='tf-idf', ngrams=None,\r\n)\r\nvectorizer.adapt(text_dataset.batch(1024))\r\n\r\n# Create model\r\nmodel = tf.keras.models.Sequential()\r\nmodel.add(tf.keras.Input(shape=(1,), dtype=tf.string))\r\nmodel.add(vectorizer)\r\n\r\nprint (vectorizer(\"sky is blue\"))\r\n\r\n# Save\r\nfilepath = \"my_model.keras\"\r\n# .tf not work, .h5 not work, .keras not work\r\nmodel.save(filepath)\r\n\r\n# Load\r\nloaded_model = tf.keras.models.load_model(filepath)\r\nloaded_vectorizer = loaded_model.layers[0]\r\n\r\nprint (loaded_vectorizer(\"sky is blue\"))\r\n```\r\n\r\nAny ideas how to save the model with TextVectorizer in tensorflow 2.16.1 now?",
"Hi **@Expert73** ,\r\n\r\nSorry for the dealy, I reproduced the code you shared. Here i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/8d20d7d01900b8be55662d751c91b80f/63365_2-16-1-v.ipynb) for how to save the model with TextVectorizer in tensorflow 2.16.1 for your reference. please go through it once.\r\n\r\nThank you!",
"> Hi **@Expert73** ,\r\n> \r\n> Sorry for the dealy, I reproduced the code you shared. Here i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/8d20d7d01900b8be55662d751c91b80f/63365_2-16-1-v.ipynb) for how to save the model with TextVectorizer in tensorflow 2.16.1 for your reference. please go through it once.\r\n> \r\n> Thank you!\r\n\r\nColab work fine. But in windows 10:\r\n```\r\nD:\\project_1\\venv\\Scripts\\python.exe D:\\project_1\\test.py \r\n2024-03-21 20:54:53.563758: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2024-03-21 20:54:54.181494: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2.16.1\r\n2024-03-21 20:54:55.382961: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 250ms/step - loss: 0.5000\r\nTraceback (most recent call last):\r\n File \"D:\\project_1\\test.py\", line 47, in <module>\r\n loaded_model = tf.keras.models.load_model(\"my_model_with_vectorizer.keras\")\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"D:\\project_1\\venv\\Lib\\site-packages\\keras\\src\\saving\\saving_api.py\", line 176, in load_model\r\n return saving_lib.load_model(\r\n ^^^^^^^^^^^^^^^^^^^^^^\r\n File \"D:\\project_1\\venv\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py\", line 192, in load_model\r\n _raise_loading_failure(error_msgs)\r\n File \"D:\\project_1\\venv\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py\", line 273, in _raise_loading_failure\r\n raise ValueError(msg)\r\nValueError: A total of 1 objects could not be loaded. Example error message for object <keras.src.optimizers.adam.Adam object at 0x00000214B810A8A0>:\r\n\r\n'Unable to synchronously open object (bad object header version number)'\r\n\r\nList of objects that could not be loaded:\r\n[<keras.src.optimizers.adam.Adam object at 0x00000214B810A8A0>]\r\n\r\nProcess finished with exit code 1\r\n```\r\nWindows 10 x64\r\nPython 3.12\r\nTensorflow 2.16.1\r\n\r\ncolab\r\npython 3.10.12 (!)\r\nTensorflow 2.16.1\r\n\r\nUpdate:\r\ntest with python 3.10 and 3.11, tensorflow 2.16.1 in windows - work fine.\r\n\r\nproblem with python 3.12 (!)\r\n\r\nhow can I solve the problem?\r\n",
"Problem still with python 3.12",
"> > Hi **@Expert73** ,\r\n> > Sorry for the dealy, I reproduced the code you shared. Here i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/8d20d7d01900b8be55662d751c91b80f/63365_2-16-1-v.ipynb) for how to save the model with TextVectorizer in tensorflow 2.16.1 for your reference. please go through it once.\r\n> > Thank you!\r\n> \r\n> Colab work fine. But in windows 10:\r\n> \r\n> ```\r\n> D:\\project_1\\venv\\Scripts\\python.exe D:\\project_1\\test.py \r\n> 2024-03-21 20:54:53.563758: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n> 2024-03-21 20:54:54.181494: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n> 2.16.1\r\n> 2024-03-21 20:54:55.382961: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\n> To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n> 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 250ms/step - loss: 0.5000\r\n> Traceback (most recent call last):\r\n> File \"D:\\project_1\\test.py\", line 47, in <module>\r\n> loaded_model = tf.keras.models.load_model(\"my_model_with_vectorizer.keras\")\r\n> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n> File \"D:\\project_1\\venv\\Lib\\site-packages\\keras\\src\\saving\\saving_api.py\", line 176, in load_model\r\n> return saving_lib.load_model(\r\n> ^^^^^^^^^^^^^^^^^^^^^^\r\n> File \"D:\\project_1\\venv\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py\", line 192, in load_model\r\n> _raise_loading_failure(error_msgs)\r\n> File \"D:\\project_1\\venv\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py\", line 273, in _raise_loading_failure\r\n> raise ValueError(msg)\r\n> ValueError: A total of 1 objects could not be loaded. Example error message for object <keras.src.optimizers.adam.Adam object at 0x00000214B810A8A0>:\r\n> \r\n> 'Unable to synchronously open object (bad object header version number)'\r\n> \r\n> List of objects that could not be loaded:\r\n> [<keras.src.optimizers.adam.Adam object at 0x00000214B810A8A0>]\r\n> \r\n> Process finished with exit code 1\r\n> ```\r\n> \r\n> Windows 10 x64 Python 3.12 Tensorflow 2.16.1\r\n> \r\n> colab python 3.10.12 (!) Tensorflow 2.16.1\r\n> \r\n> Update: test with python 3.10 and 3.11, tensorflow 2.16.1 in windows - work fine.\r\n> \r\n> problem with python 3.12 (!)\r\n> \r\n> how can I solve the problem?\r\n\r\nyou are the best bro!!",
"@Expert73 try Python 3.12.2.\r\n\r\nAlmost the same issue when I used Python 3.12.0 + Keras 3.1.1 + TensorFlow 2.16.1\r\n```\r\nValueError: A total of 1 objects could not be loaded. Example error message for object <keras.src.optimizers.rmsprop.RMSprop object at 0x12732ec30>:\r\n\r\n'Unable to open object (bad object header version number)'\r\n\r\nList of objects that could not be loaded:\r\n[<keras.src.optimizers.rmsprop.RMSprop object at 0x12732ec30>]\r\n```\r\nWhen I changed my Python version from `3.12.0` to `3.12.2`, it worked well.",
"> @Expert73 try Python 3.12.2.\r\n> \r\n> Almost the same issue when I used Python 3.12.0 + Keras 3.1.1 + TensorFlow 2.16.1\r\n> \r\n> ```\r\n> ValueError: A total of 1 objects could not be loaded. Example error message for object <keras.src.optimizers.rmsprop.RMSprop object at 0x12732ec30>:\r\n> \r\n> 'Unable to open object (bad object header version number)'\r\n> \r\n> List of objects that could not be loaded:\r\n> [<keras.src.optimizers.rmsprop.RMSprop object at 0x12732ec30>]\r\n> ```\r\n> \r\n> When I changed my Python version from `3.12.0` to `3.12.2`, it worked well.\r\n\r\n@Venkat6871 ",
"> When I changed my Python version from `3.12.0` to `3.12.2`, it worked well.\r\n\r\nThanks for the info. I checked too. Everything works great!",
"Hi **@Expert73** ,\r\n- Great to see it is working well to you. Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63365\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63365\">No</a>\n"
] | 2024-03-10T04:07:46 | 2024-06-07T01:51:14 | 2024-06-07T01:51:11 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
windows 10
### Mobile device
_No response_
### Python version
3.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Model saved from Python 3.12 tensorflow 2.16.1
model.save('my_model.keras', overwrite=True)
After this the model does not load
### Standalone code to reproduce the issue
```shell
model=tf.keras.models.load_model('my_model.keras', custom_objects=None, compile=True, safe_mode=True)
```
### Relevant log output
```shell
Traceback (most recent call last):
File "D:\Project\main.py", line 391, in <module>
model=tf.keras.models.load_model('my_model.keras', custom_objects=None, compile=True, safe_mode=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Project\venv\Lib\site-packages\keras\src\saving\saving_api.py", line 176, in load_model
return saving_lib.load_model(
^^^^^^^^^^^^^^^^^^^^^^
File "D:\Project\venv\Lib\site-packages\keras\src\saving\saving_lib.py", line 192, in load_model
_raise_loading_failure(error_msgs)
File "D:\Project\venv\Lib\site-packages\keras\src\saving\saving_lib.py", line 273, in _raise_loading_failure
raise ValueError(msg)
ValueError: A total of 13 objects could not be loaded. Example error message for object <Sequential name=sequential, built=True>:
'Unable to synchronously open object (bad local heap signature)'
List of objects that could not be loaded:
[<Sequential name=sequential, built=True>, <TextVectorization name=text_vectorization, built=True>, <StringLookup name=string_lookup_1, built=False>, <Embedding name=embedding, built=True>, <Conv1D name=conv1d, built=True>, <Dropout name=dropout, built=True>, <Conv1D name=conv1d_1, built=True>, <Dropout name=dropout_1, built=True>, <GlobalMaxPooling1D name=global_max_pooling1d, built=True>, <Dense name=dense, built=True>, <Dropout name=dropout_2, built=True>, <Dense name=dense_1, built=True>, <keras.src.optimizers.adam.Adam object at 0x000001C5026B24E0>]
```
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[
" if you have a function or variable named Zeros in your code and you also import tensorflow, which also defines a symbol named Zeros, it can lead to this conflict.",
"@athenax246,\r\nCould you please provide the steps you followed to install the tensorflow on the Windows machine. With the error I assume that the tensorflow was not properly installed and also while installation the compatible tested build configurations have to follow for the smooth installation.\r\nhttps://www.tensorflow.org/install/source_windows\r\n\r\nThank you!",
"Thanks for you reply, I am using tensorflow in the Anaconda environment.\r\nFor installation, I used:\r\nconda create --name tf_cpu\r\nconda activate tf_cpu\r\n",
"@athenax246,\r\nI tried to install the tensorflow v2.13 on Windows 11 using conda and it was installed without any issue/error and also I am able to import the tensorflow. Kindly find the screenshot for the reference.\r\n\r\nThank you!",
"Problem solved, thanks so much!",
"@athenax246,\r\nGlad the issue is resolved for you, Could you please feel free to move this to closed status. Thank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63363\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63363\">No</a>\n"
] | 2024-03-10T01:09:44 | 2024-03-19T16:05:26 | 2024-03-19T16:05:22 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.13.0
### Custom code
Yes
### OS platform and distribution
Win 11
### Mobile device
_No response_
### Python version
3.9.7
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I intalled tensorflow, but it gives an error when I try to import it.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
```
### Relevant log output
```shell
runfile('X:/Nano-Photonics and Quantum Optics Lab!/ML Project/Tkinter learning/Tkinter Git - GitLab/Inverse_Design_Periodic_GUI_CustomModern.py', wdir='X:/Nano-Photonics and Quantum Optics Lab!/ML Project/Tkinter learning/Tkinter Git - GitLab')
Traceback (most recent call last):
File "X:\Nano-Photonics and Quantum Optics Lab!\ML Project\Tkinter learning\Tkinter Git - GitLab\Inverse_Design_Periodic_GUI_CustomModern.py", line 20, in <module>
import tensorflow as tf #print(tf.__version__)
File "C:\Users\athen\anaconda3\lib\site-packages\tensorflow\__init__.py", line 469, in <module>
_keras._load()
File "C:\Users\athen\anaconda3\lib\site-packages\tensorflow\python\util\lazy_loader.py", line 41, in _load
module = importlib.import_module(self.__name__)
File "C:\Users\athen\anaconda3\lib\importlib\__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "C:\Users\athen\anaconda3\lib\site-packages\keras\__init__.py", line 20, in <module>
from keras import distribute
File "C:\Users\athen\anaconda3\lib\site-packages\keras\distribute\__init__.py", line 18, in <module>
from keras.distribute import sidecar_evaluator
File "C:\Users\athen\anaconda3\lib\site-packages\keras\distribute\sidecar_evaluator.py", line 22, in <module>
from keras.optimizers.optimizer_experimental import (
File "C:\Users\athen\anaconda3\lib\site-packages\keras\optimizers\__init__.py", line 25, in <module>
from keras import backend
File "C:\Users\athen\anaconda3\lib\site-packages\keras\backend\__init__.py", line 3, in <module>
from keras.backend import experimental
File "C:\Users\athen\anaconda3\lib\site-packages\keras\backend\experimental\__init__.py", line 3, in <module>
from keras.src.backend import disable_tf_random_generator
File "C:\Users\athen\anaconda3\lib\site-packages\keras\src\__init__.py", line 21, in <module>
from keras.src import applications
File "C:\Users\athen\anaconda3\lib\site-packages\keras\src\applications\__init__.py", line 18, in <module>
from keras.src.applications.convnext import ConvNeXtBase
File "C:\Users\athen\anaconda3\lib\site-packages\keras\src\applications\convnext.py", line 28, in <module>
from keras.src import backend
File "C:\Users\athen\anaconda3\lib\site-packages\keras\src\backend.py", line 35, in <module>
from keras.src.engine import keras_tensor
File "C:\Users\athen\anaconda3\lib\site-packages\keras\src\engine\keras_tensor.py", line 19, in <module>
from keras.src.utils import object_identity
File "C:\Users\athen\anaconda3\lib\site-packages\keras\src\utils\__init__.py", line 20, in <module>
from keras.src.saving.serialization_lib import deserialize_keras_object
File "C:\Users\athen\anaconda3\lib\site-packages\keras\src\saving\serialization_lib.py", line 28, in <module>
from keras.src.saving.legacy.saved_model.utils import in_tf_saved_model_scope
File "C:\Users\athen\anaconda3\lib\site-packages\keras\src\saving\legacy\saved_model\utils.py", line 30, in <module>
from keras.src.utils.layer_utils import CallFunctionSpec
File "C:\Users\athen\anaconda3\lib\site-packages\keras\src\utils\layer_utils.py", line 26, in <module>
from keras.src import initializers
File "C:\Users\athen\anaconda3\lib\site-packages\keras\src\initializers\__init__.py", line 23, in <module>
from keras.src.initializers import initializers_v1
File "C:\Users\athen\anaconda3\lib\site-packages\keras\src\initializers\initializers_v1.py", line 32, in <module>
keras_export(v1=["keras.initializers.Zeros", "keras.initializers.zeros"])(
File "C:\Users\athen\anaconda3\lib\site-packages\tensorflow\python\util\tf_export.py", line 348, in __call__
self.set_attr(undecorated_func, api_names_attr, self._names)
File "C:\Users\athen\anaconda3\lib\site-packages\tensorflow\python\util\tf_export.py", line 363, in set_attr
raise SymbolAlreadyExposedError(
SymbolAlreadyExposedError: Symbol Zeros is already exposed as ().
```
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I_kwDOArmXAs6ByV3L
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TF 2.16.1 Fails to work with GPUs
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[
"It does not work with python=3.12.2 either. Same error. installed tensorflow with `$ pip install tensorflow[and-cuda]`",
"The same error on bare Ubuntu and WSL2 2.15 works without any problems with python 3.11",
"I have the same problem with **Ubuntu 22.04.4** with the following environment:\r\n- `tensorflow==2.16.1`\r\n- `Python 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] on linux`\r\n- cuDNN 8.6.0.163\r\n- `gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0`\r\n\r\n`nvcc --version` output:\r\n```sh\r\nnvcc: NVIDIA (R) Cuda compiler driver\r\nCopyright (c) 2005-2022 NVIDIA Corporation\r\nBuilt on Wed_Sep_21_10:33:58_PDT_2022\r\nCuda compilation tools, release 11.8, V11.8.89\r\nBuild cuda_11.8.r11.8/compiler.31833905_0\r\n```",
"I'm not sure if this is the root cause, but I resolved my own issue which also surfaced as a \"Cannot dlopen some GPU libraries.\" error when trying to run `python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"`\r\n\r\nTo resolve my issue, I followed the tested build versions here:\r\nhttps://www.tensorflow.org/install/source#gpu\r\n\r\nand I needed to update my existing installations from cuDNN 9 -> 8.9 and CUDA 12.4->12.3\r\n\r\nWhen you're on an [NVIDIA download page like this one for CUDA Toolkit](https://developer.nvidia.com/cuda-downloads), don't just download the latest version. See previous versions by hitting \"Archive of Previous CUDA Releases\"\r\n\r\n@JuanVargas can you try uninstalling your existing CUDA installation to a tested build configuration for TF 2.16 by downgrading to CUDA 12.3?\r\n\r\nI followed this post to uninstall my existing cuda installation:\r\nhttps://askubuntu.com/questions/530043/removing-nvidia-cuda-toolkit-and-installing-new-one\r\n\r\n@DiegoMont can you try upgrading your cuDNN to 8.9 and CUDA to 12.3?",
"I am having the same issue. Brand new Ubuntu 22.04 WSL2 image. Blank Conda environment with either `python 3.12.*` or `3.11.*` fails to correctly setup tensorflow for GPU use when following the recommended:\r\n`pip install tensorflow[and-cuda]`\r\n\r\nTrying to list the physical devices results in:\r\n```\r\n2024-03-11 02:00:00.294704: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-03-11 02:00:00.709325: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-03-11 02:00:01.180225: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:2d:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-11 02:00:01.180445: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\n[]\r\n```\r\n```\r\ncuDNN 8.9.*\r\nCuda 12.3\r\nTensorflow 2.16.1\r\nTensorRT 8.6.1\r\n```\r\n\r\nIs this a new issue caused by the fact that it doesn't appear that any system cuda needs to be separately installed in WSL2 anymore. I certainly didn't install one manually and yet `nvidia-smi` is happily reporting cuda version 12.3. It probably comes down to some env paths not set correctly but playing around with $CUDA_PATH and guessing the location within the conda environment has not resolved anything. TensorRT doesn't seem to be picked up yet is definitely installed in the conda environment. Pytorch GPU visibility works as expected.",
"Hi @JuanVargas ,\r\n\r\nFor GPU package you need to ensure the installation of CUDA driver which can be verified with nvidia-smi command. Then you need to install TF-cuda package with `pip install tensorflow[and-cuda]` which automatically installs required cuda/cudnn libraries.\r\n\r\nI have checked in colab and able to detect GPU.Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/808553205834b3043b619980c9ae3ffc/63362.ipynb).",
"\r\ndoublequotes in pip install because of ZSH \r\n\r\n```\r\npip install \"tensorflow[and-cuda]==2.16.1\" \r\n \r\n\r\nLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\r\nRequirement already satisfied: tensorflow==2.16.1 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (2.16.1)\r\nRequirement already satisfied: absl-py>=1.0.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (2.1.0)\r\nRequirement already satisfied: astunparse>=1.6.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (1.6.3)\r\nRequirement already satisfied: flatbuffers>=23.5.26 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (24.3.7)\r\nRequirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (0.5.4)\r\nRequirement already satisfied: google-pasta>=0.1.1 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (0.2.0)\r\nRequirement already satisfied: h5py>=3.10.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (3.10.0)\r\nRequirement already satisfied: libclang>=13.0.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (16.0.6)\r\nRequirement already satisfied: ml-dtypes~=0.3.1 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (0.3.2)\r\nRequirement already satisfied: opt-einsum>=2.3.2 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (3.3.0)\r\nRequirement already satisfied: packaging in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (24.0)\r\nRequirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (4.25.3)\r\nRequirement already satisfied: requests<3,>=2.21.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (2.31.0)\r\nRequirement already satisfied: setuptools in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (69.1.1)\r\nRequirement already satisfied: six>=1.12.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (1.16.0)\r\nRequirement already satisfied: termcolor>=1.1.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (2.4.0)\r\nRequirement already satisfied: typing-extensions>=3.6.6 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (4.10.0)\r\nRequirement already satisfied: wrapt>=1.11.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (1.16.0)\r\nRequirement already satisfied: grpcio<2.0,>=1.24.3 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (1.62.1)\r\nRequirement already satisfied: tensorboard<2.17,>=2.16 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (2.16.2)\r\nRequirement already satisfied: keras>=3.0.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (3.0.5)\r\nRequirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (0.36.0)\r\nRequirement already satisfied: numpy<2.0.0,>=1.23.5 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (1.26.4)\r\nRequirement already satisfied: nvidia-cublas-cu12==12.3.4.1 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (12.3.4.1)\r\nRequirement already satisfied: nvidia-cuda-cupti-cu12==12.3.101 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (12.3.101)\r\nRequirement already satisfied: nvidia-cuda-nvcc-cu12==12.3.107 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (12.3.107)\r\nRequirement already satisfied: nvidia-cuda-nvrtc-cu12==12.3.107 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (12.3.107)\r\nRequirement already satisfied: nvidia-cuda-runtime-cu12==12.3.101 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (12.3.101)\r\nRequirement already satisfied: nvidia-cudnn-cu12==8.9.7.29 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (8.9.7.29)\r\nRequirement already satisfied: nvidia-cufft-cu12==11.0.12.1 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (11.0.12.1)\r\nRequirement already satisfied: nvidia-curand-cu12==10.3.4.107 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (10.3.4.107)\r\nRequirement already satisfied: nvidia-cusolver-cu12==11.5.4.101 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (11.5.4.101)\r\nRequirement already satisfied: nvidia-cusparse-cu12==12.2.0.103 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (12.2.0.103)\r\nRequirement already satisfied: nvidia-nccl-cu12==2.19.3 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (2.19.3)\r\nRequirement already satisfied: nvidia-nvjitlink-cu12==12.3.101 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorflow[and-cuda]==2.16.1) (12.3.101)\r\nRequirement already satisfied: wheel<1.0,>=0.23.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from astunparse>=1.6.0->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (0.42.0)\r\nRequirement already satisfied: rich in ./miniconda3/envs/tf/lib/python3.11/site-packages (from keras>=3.0.0->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (13.7.1)\r\nRequirement already satisfied: namex in ./miniconda3/envs/tf/lib/python3.11/site-packages (from keras>=3.0.0->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (0.0.7)\r\nRequirement already satisfied: dm-tree in ./miniconda3/envs/tf/lib/python3.11/site-packages (from keras>=3.0.0->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (0.1.8)\r\nRequirement already satisfied: charset-normalizer<4,>=2 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (3.3.2)\r\nRequirement already satisfied: idna<4,>=2.5 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (3.6)\r\nRequirement already satisfied: urllib3<3,>=1.21.1 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (2.2.1)\r\nRequirement already satisfied: certifi>=2017.4.17 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (2024.2.2)\r\nRequirement already satisfied: markdown>=2.6.8 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorboard<2.17,>=2.16->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (3.5.2)\r\nRequirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorboard<2.17,>=2.16->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (0.7.2)\r\nRequirement already satisfied: werkzeug>=1.0.1 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from tensorboard<2.17,>=2.16->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (3.0.1)\r\nRequirement already satisfied: MarkupSafe>=2.1.1 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (2.1.5)\r\nRequirement already satisfied: markdown-it-py>=2.2.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from rich->keras>=3.0.0->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (3.0.0)\r\nRequirement already satisfied: pygments<3.0.0,>=2.13.0 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from rich->keras>=3.0.0->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (2.17.2)\r\nRequirement already satisfied: mdurl~=0.1 in ./miniconda3/envs/tf/lib/python3.11/site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow==2.16.1->tensorflow[and-cuda]==2.16.1) (0.1.2)\r\n```\r\n\r\n\r\n```\r\nnvidia-smi \r\n \r\n+-----------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 550.60.01 Driver Version: 551.76 CUDA Version: 12.4 |\r\n|-----------------------------------------+------------------------+----------------------+\r\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+========================+======================|\r\n| 0 NVIDIA GeForce RTX 4070 Ti On | 00000000:01:00.0 On | N/A |\r\n| 0% 39C P5 10W / 285W | 4334MiB / 12282MiB | 13% Default |\r\n| | | N/A |\r\n+-----------------------------------------+------------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=========================================================================================|\r\n| 0 N/A N/A 41 G /Xwayland N/A |\r\n+-----------------------------------------------------------------------------------------+\r\n\r\n```\r\n\r\n```\r\npython3\r\n\r\nPython 3.11.7 (main, Dec 15 2023, 18:12:31) [GCC 11.2.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import tensorflow as tf\r\nprint(tf.config.list_physical_devices('GPU'))2024-03-11 09:36:29.601060: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2024-03-11 09:36:29.921637: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-03-11 09:36:30.793353: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n>>> print(tf.config.list_physical_devices('GPU'))\r\n2024-03-11 09:36:33.878560: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-11 09:36:33.980099: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\n[]\r\n>>>\r\n```",
"```\r\nnvcc -V \r\n \r\nnvcc: NVIDIA (R) Cuda compiler driver\r\nCopyright (c) 2005-2024 NVIDIA Corporation\r\nBuilt on Tue_Feb_27_16:19:38_PST_2024\r\nCuda compilation tools, release 12.4, V12.4.99\r\nBuild cuda_12.4.r12.4/compiler.33961263_0\r\n```",
"got it work :) first \r\nhttps://developer.nvidia.com/rdp/cudnn-archive?source=post_page-----bfbeb77e7c89-------------------------------- \r\n\r\nthen download [Local Installer for Ubuntu22.04 x86_64 (Deb)](https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.7/local_installers/12.x/cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb/) \r\n\r\nunpack and install libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb \r\n\r\n\r\n\r\n```\r\nsudo dpkg -i libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb \r\n \r\nSelecting previously unselected package libcudnn8.\r\n(Reading database ... 47318 files and directories currently installed.)\r\nPreparing to unpack libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb ...\r\nUnpacking libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\nSetting up libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\n\r\n```\r\n\r\n\r\n```\r\npython3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\" \r\n\r\n \r\n2024-03-11 10:27:47.879686: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2024-03-11 10:27:47.909157: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-03-11 10:27:48.316717: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-03-11 10:27:48.664469: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-11 10:27:48.688059: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-11 10:27:48.688111: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n\r\n```",
"Hi Krzysztof\r\n\r\nI visited the site\r\nhttps://developer.nvidia.com/rdp/cudnn-archive?source=post_page-----bfbeb77e7c89--------------------------------\r\n\r\nwhere I found an entry listed as \" Local Installer for UBuntu22.04\r\nx86_64(Deb)\" which I downloaded.\r\nUnfortunately what I got is a package named\r\n\"cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb\"\r\nwhich is not the same as the name you suggest in your message, which is \"\r\nlibcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\"\r\n\r\nI assume what you meant is to get the libcudnn8_8.9.7.29*amd64.deb and\r\nthe cuda12.2_amd64.deb separately and install both.\r\n\r\nI have CUDA 12.4. I will not go back to trying to make TF 2.16.1 work with\r\nolder versions of CUDA (12.2 or 12.3) because sooner or later\r\nthe TF team will have to produce a version with the updated version of\r\nCUDA. IMHO, rather than us wasting time going back in versions,\r\nthe TF beak should invest time going forward to update TF to the current\r\nCUDA version.\r\n\r\nThank you, Juan\r\n\r\n\r\nOn Mon, Mar 11, 2024 at 5:30 AM Krzysztof Radzikowski <\r\n***@***.***> wrote:\r\n\r\n> got it work :) first\r\n>\r\n> https://developer.nvidia.com/rdp/cudnn-archive?source=post_page-----bfbeb77e7c89--------------------------------\r\n>\r\n> then download Local Installer for Ubuntu22.04 x86_64 (Deb)\r\n> <https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.7/local_installers/12.x/cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb/>\r\n>\r\n> unpack and install libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\r\n>\r\n> `\r\n>\r\n> sudo dpkg -i libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\r\n> Selecting previously unselected package libcudnn8.\r\n> (Reading database ... 47318 files and directories currently installed.)\r\n> Preparing to unpack libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb ...\r\n> Unpacking libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\n> Setting up libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\n>\r\n> `\r\n>\r\n> python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n>\r\n>\r\n> 2024-03-11 10:27:47.879686: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n> 2024-03-11 10:27:47.909157: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\n> To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n> 2024-03-11 10:27:48.316717: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n> 2024-03-11 10:27:48.664469: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> Your kernel may have been built without NUMA support.\r\n> 2024-03-11 10:27:48.688059: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> Your kernel may have been built without NUMA support.\r\n> 2024-03-11 10:27:48.688111: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> Your kernel may have been built without NUMA support.\r\n> [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n>\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1987970615>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AAGK34PV2IIF5FUZ73EPKOTYXV2SZAVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBXHE3TANRRGU>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"It's just tensorflow can't see the Cuda libraries.\r\n\r\nInstal tensorflow[and-cuda] and add this to your .bashrc or conda activation script. Adjust python version in it according to your setup.\r\n\r\nNVIDIA_PACKAGE_DIR=\"$CONDA_PREFIX/lib/python3.12/site-packages/nvidia\"\r\n\r\nfor dir in $NVIDIA_PACKAGE_DIR/*; do\r\n if [ -d \"$dir/lib\" ]; then\r\n export LD_LIBRARY_PATH=\"$dir/lib:$LD_LIBRARY_PATH\"\r\n fi\r\ndone\r\n\r\n\r\nYou won't need to install cuda or cudnn on the system. only the cuda libraries that are installed with $ pip install tensorflow[and-cuda] would be enough.\r\n\r\n\r\nOn Mon, Mar 11, 2024, 7:04 a.m. Juan E. Vargas ***@***.***>\r\nwrote:\r\n\r\n> Hi Krzysztof\r\n>\r\n> I visited the site\r\n>\r\n> https://developer.nvidia.com/rdp/cudnn-archive?source=post_page-----bfbeb77e7c89--------------------------------\r\n>\r\n> where I found an entry listed as \" Local Installer for UBuntu22.04\r\n> x86_64(Deb)\" which I downloaded.\r\n> Unfortunately what I got is a package named\r\n> \"cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb\"\r\n> which is not the same as the name you suggest in your message, which is \"\r\n> libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\"\r\n>\r\n> I assume what you meant is to get the libcudnn8_8.9.7.29*amd64.deb and\r\n> the cuda12.2_amd64.deb separately and install both.\r\n>\r\n> I have CUDA 12.4. I will not go back to trying to make TF 2.16.1 work with\r\n> older versions of CUDA (12.2 or 12.3) because sooner or later\r\n> the TF team will have to produce a version with the updated version of\r\n> CUDA. IMHO, rather than us wasting time going back in versions,\r\n> the TF beak should invest time going forward to update TF to the current\r\n> CUDA version.\r\n>\r\n> Thank you, Juan\r\n>\r\n>\r\n> On Mon, Mar 11, 2024 at 5:30 AM Krzysztof Radzikowski <\r\n> ***@***.***> wrote:\r\n>\r\n> > got it work :) first\r\n> >\r\n> >\r\n> https://developer.nvidia.com/rdp/cudnn-archive?source=post_page-----bfbeb77e7c89--------------------------------\r\n> >\r\n> > then download Local Installer for Ubuntu22.04 x86_64 (Deb)\r\n> > <\r\n> https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.7/local_installers/12.x/cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb/>\r\n>\r\n> >\r\n> > unpack and install libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\r\n> >\r\n> > `\r\n> >\r\n> > sudo dpkg -i libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\r\n> > Selecting previously unselected package libcudnn8.\r\n> > (Reading database ... 47318 files and directories currently installed.)\r\n> > Preparing to unpack libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb ...\r\n> > Unpacking libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\n> > Setting up libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\n> >\r\n> > `\r\n> >\r\n> > python3 -c \"import tensorflow as tf;\r\n> print(tf.config.list_physical_devices('GPU'))\"\r\n> >\r\n> >\r\n> > 2024-03-11 10:27:47.879686: I tensorflow/core/util/port.cc:113] oneDNN\r\n> custom operations are on. You may see slightly different numerical results\r\n> due to floating-point round-off errors from different computation orders.\r\n> To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n> > 2024-03-11 10:27:47.909157: I\r\n> tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary\r\n> is optimized to use available CPU instructions in performance-critical\r\n> operations.\r\n> > To enable the following instructions: AVX2 AVX_VNNI FMA, in other\r\n> operations, rebuild TensorFlow with the appropriate compiler flags.\r\n> > 2024-03-11 10:27:48.316717: W\r\n> tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could\r\n> not find TensorRT\r\n> > 2024-03-11 10:27:48.664469: I\r\n> external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not\r\n> open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> > Your kernel may have been built without NUMA support.\r\n> > 2024-03-11 10:27:48.688059: I\r\n> external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not\r\n> open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> > Your kernel may have been built without NUMA support.\r\n> > 2024-03-11 10:27:48.688111: I\r\n> external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not\r\n> open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> > Your kernel may have been built without NUMA support.\r\n> > [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n> >\r\n> >\r\n> > —\r\n> > Reply to this email directly, view it on GitHub\r\n> > <\r\n> https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1987970615>,\r\n>\r\n> > or unsubscribe\r\n> > <\r\n> https://github.com/notifications/unsubscribe-auth/AAGK34PV2IIF5FUZ73EPKOTYXV2SZAVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBXHE3TANRRGU>\r\n>\r\n> > .\r\n> > You are receiving this because you were mentioned.Message ID:\r\n> > ***@***.***>\r\n> >\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1988394624>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AZRPJAGFJU5ZGBGHOSUK6DTYXWTU3AVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBYGM4TINRSGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"will try that and will let you know. Thank you for the suggestion. Juan\r\n\r\n\r\nOn Mon, Mar 11, 2024 at 10:52 AM Shayan Shahrokhi ***@***.***>\r\nwrote:\r\n\r\n> It's just tensorflow can't see the Cuda libraries.\r\n>\r\n> Instal tensorflow[and-cuda] and add this to your .bashrc or conda\r\n> activation script\r\n>\r\n> NVIDIA_PACKAGE_DIR=\"$CONDA_PREFIX/lib/python3.12/site-packages/nvidia\"\r\n>\r\n> for dir in $NVIDIA_PACKAGE_DIR/*; do\r\n> if [ -d \"$dir/lib\" ]; then\r\n> export LD_LIBRARY_PATH=\"$dir/lib:$LD_LIBRARY_PATH\"\r\n> fi\r\n> done\r\n>\r\n>\r\n>\r\n> On Mon, Mar 11, 2024, 7:04 a.m. Juan E. Vargas ***@***.***>\r\n> wrote:\r\n>\r\n> > Hi Krzysztof\r\n> >\r\n> > I visited the site\r\n> >\r\n> >\r\n> https://developer.nvidia.com/rdp/cudnn-archive?source=post_page-----bfbeb77e7c89--------------------------------\r\n> >\r\n> > where I found an entry listed as \" Local Installer for UBuntu22.04\r\n> > x86_64(Deb)\" which I downloaded.\r\n> > Unfortunately what I got is a package named\r\n> > \"cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb\"\r\n> > which is not the same as the name you suggest in your message, which is\r\n> \"\r\n> > libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\"\r\n> >\r\n> > I assume what you meant is to get the libcudnn8_8.9.7.29*amd64.deb and\r\n> > the cuda12.2_amd64.deb separately and install both.\r\n> >\r\n> > I have CUDA 12.4. I will not go back to trying to make TF 2.16.1 work\r\n> with\r\n> > older versions of CUDA (12.2 or 12.3) because sooner or later\r\n> > the TF team will have to produce a version with the updated version of\r\n> > CUDA. IMHO, rather than us wasting time going back in versions,\r\n> > the TF beak should invest time going forward to update TF to the current\r\n> > CUDA version.\r\n> >\r\n> > Thank you, Juan\r\n> >\r\n> >\r\n> > On Mon, Mar 11, 2024 at 5:30 AM Krzysztof Radzikowski <\r\n> > ***@***.***> wrote:\r\n> >\r\n> > > got it work :) first\r\n> > >\r\n> > >\r\n> >\r\n> https://developer.nvidia.com/rdp/cudnn-archive?source=post_page-----bfbeb77e7c89--------------------------------\r\n> > >\r\n> > > then download Local Installer for Ubuntu22.04 x86_64 (Deb)\r\n> > > <\r\n> >\r\n> https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.7/local_installers/12.x/cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb/>\r\n>\r\n> >\r\n> > >\r\n> > > unpack and install libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\r\n> > >\r\n> > > `\r\n> > >\r\n> > > sudo dpkg -i libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\r\n> > > Selecting previously unselected package libcudnn8.\r\n> > > (Reading database ... 47318 files and directories currently\r\n> installed.)\r\n> > > Preparing to unpack libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb ...\r\n> > > Unpacking libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\n> > > Setting up libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\n> > >\r\n> > > `\r\n> > >\r\n> > > python3 -c \"import tensorflow as tf;\r\n> > print(tf.config.list_physical_devices('GPU'))\"\r\n> > >\r\n> > >\r\n> > > 2024-03-11 10:27:47.879686: I tensorflow/core/util/port.cc:113] oneDNN\r\n> > custom operations are on. You may see slightly different numerical\r\n> results\r\n> > due to floating-point round-off errors from different computation\r\n> orders.\r\n> > To turn them off, set the environment variable\r\n> `TF_ENABLE_ONEDNN_OPTS=0`.\r\n> > > 2024-03-11 10:27:47.909157: I\r\n> > tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow\r\n> binary\r\n> > is optimized to use available CPU instructions in performance-critical\r\n> > operations.\r\n> > > To enable the following instructions: AVX2 AVX_VNNI FMA, in other\r\n> > operations, rebuild TensorFlow with the appropriate compiler flags.\r\n> > > 2024-03-11 10:27:48.316717: W\r\n> > tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning:\r\n> Could\r\n> > not find TensorRT\r\n> > > 2024-03-11 10:27:48.664469: I\r\n> > external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could\r\n> not\r\n> > open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> > > Your kernel may have been built without NUMA support.\r\n> > > 2024-03-11 10:27:48.688059: I\r\n> > external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could\r\n> not\r\n> > open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> > > Your kernel may have been built without NUMA support.\r\n> > > 2024-03-11 10:27:48.688111: I\r\n> > external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could\r\n> not\r\n> > open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> > > Your kernel may have been built without NUMA support.\r\n> > > [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n> > >\r\n> > >\r\n> > > —\r\n> > > Reply to this email directly, view it on GitHub\r\n> > > <\r\n> >\r\n> https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1987970615>,\r\n>\r\n> >\r\n> > > or unsubscribe\r\n> > > <\r\n> >\r\n> https://github.com/notifications/unsubscribe-auth/AAGK34PV2IIF5FUZ73EPKOTYXV2SZAVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBXHE3TANRRGU>\r\n>\r\n> >\r\n> > > .\r\n> > > You are receiving this because you were mentioned.Message ID:\r\n> > > ***@***.***>\r\n> > >\r\n> >\r\n> > —\r\n> > Reply to this email directly, view it on GitHub\r\n> > <\r\n> https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1988394624>,\r\n>\r\n> > or unsubscribe\r\n> > <\r\n> https://github.com/notifications/unsubscribe-auth/AZRPJAGFJU5ZGBGHOSUK6DTYXWTU3AVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBYGM4TINRSGQ>\r\n>\r\n> > .\r\n> > You are receiving this because you commented.Message ID:\r\n> > ***@***.***>\r\n> >\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1988630226>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AAGK34PLSBZFKQ5AQKIZ7X3YXXAMRAVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBYGYZTAMRSGY>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"Hi Shayan Shahrokhi\r\n\r\nThank you for your suggestion (adding the location of the site-packages. I\r\nhope you would not mind if I ask :\r\n\r\nI saw that in your suggestion the name python 3.12 is listed. Is that the\r\nversion of python that you used to test TF 2.16.1 compatibility with CUDA?\r\n\r\nThank you, Juan\r\n\r\n\r\n\r\nOn Mon, Mar 11, 2024 at 11:01 AM Juan Vargas ***@***.***> wrote:\r\n\r\n> will try that and will let you know. Thank you for the suggestion. Juan\r\n>\r\n>\r\n> On Mon, Mar 11, 2024 at 10:52 AM Shayan Shahrokhi <\r\n> ***@***.***> wrote:\r\n>\r\n>> It's just tensorflow can't see the Cuda libraries.\r\n>>\r\n>> Instal tensorflow[and-cuda] and add this to your .bashrc or conda\r\n>> activation script\r\n>>\r\n>> NVIDIA_PACKAGE_DIR=\"$CONDA_PREFIX/lib/python3.12/site-packages/nvidia\"\r\n>>\r\n>> for dir in $NVIDIA_PACKAGE_DIR/*; do\r\n>> if [ -d \"$dir/lib\" ]; then\r\n>> export LD_LIBRARY_PATH=\"$dir/lib:$LD_LIBRARY_PATH\"\r\n>> fi\r\n>> done\r\n>>\r\n>>\r\n>>\r\n>> On Mon, Mar 11, 2024, 7:04 a.m. Juan E. Vargas ***@***.***>\r\n>> wrote:\r\n>>\r\n>> > Hi Krzysztof\r\n>> >\r\n>> > I visited the site\r\n>> >\r\n>> >\r\n>> https://developer.nvidia.com/rdp/cudnn-archive?source=post_page-----bfbeb77e7c89--------------------------------\r\n>> >\r\n>> > where I found an entry listed as \" Local Installer for UBuntu22.04\r\n>> > x86_64(Deb)\" which I downloaded.\r\n>> > Unfortunately what I got is a package named\r\n>> > \"cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb\"\r\n>> > which is not the same as the name you suggest in your message, which is\r\n>> \"\r\n>> > libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\"\r\n>> >\r\n>> > I assume what you meant is to get the libcudnn8_8.9.7.29*amd64.deb and\r\n>> > the cuda12.2_amd64.deb separately and install both.\r\n>> >\r\n>> > I have CUDA 12.4. I will not go back to trying to make TF 2.16.1 work\r\n>> with\r\n>> > older versions of CUDA (12.2 or 12.3) because sooner or later\r\n>> > the TF team will have to produce a version with the updated version of\r\n>> > CUDA. IMHO, rather than us wasting time going back in versions,\r\n>> > the TF beak should invest time going forward to update TF to the\r\n>> current\r\n>> > CUDA version.\r\n>> >\r\n>> > Thank you, Juan\r\n>> >\r\n>> >\r\n>> > On Mon, Mar 11, 2024 at 5:30 AM Krzysztof Radzikowski <\r\n>> > ***@***.***> wrote:\r\n>> >\r\n>> > > got it work :) first\r\n>> > >\r\n>> > >\r\n>> >\r\n>> https://developer.nvidia.com/rdp/cudnn-archive?source=post_page-----bfbeb77e7c89--------------------------------\r\n>> > >\r\n>> > > then download Local Installer for Ubuntu22.04 x86_64 (Deb)\r\n>> > > <\r\n>> >\r\n>> https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.7/local_installers/12.x/cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb/>\r\n>>\r\n>> >\r\n>> > >\r\n>> > > unpack and install libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\r\n>> > >\r\n>> > > `\r\n>> > >\r\n>> > > sudo dpkg -i libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\r\n>> > > Selecting previously unselected package libcudnn8.\r\n>> > > (Reading database ... 47318 files and directories currently\r\n>> installed.)\r\n>> > > Preparing to unpack libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb ...\r\n>> > > Unpacking libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\n>> > > Setting up libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\n>> > >\r\n>> > > `\r\n>> > >\r\n>> > > python3 -c \"import tensorflow as tf;\r\n>> > print(tf.config.list_physical_devices('GPU'))\"\r\n>> > >\r\n>> > >\r\n>> > > 2024-03-11 10:27:47.879686: I tensorflow/core/util/port.cc:113]\r\n>> oneDNN\r\n>> > custom operations are on. You may see slightly different numerical\r\n>> results\r\n>> > due to floating-point round-off errors from different computation\r\n>> orders.\r\n>> > To turn them off, set the environment variable\r\n>> `TF_ENABLE_ONEDNN_OPTS=0`.\r\n>> > > 2024-03-11 10:27:47.909157: I\r\n>> > tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow\r\n>> binary\r\n>> > is optimized to use available CPU instructions in performance-critical\r\n>> > operations.\r\n>> > > To enable the following instructions: AVX2 AVX_VNNI FMA, in other\r\n>> > operations, rebuild TensorFlow with the appropriate compiler flags.\r\n>> > > 2024-03-11 10:27:48.316717: W\r\n>> > tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning:\r\n>> Could\r\n>> > not find TensorRT\r\n>> > > 2024-03-11 10:27:48.664469: I\r\n>> > external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could\r\n>> not\r\n>> > open file to read NUMA node:\r\n>> /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n>> > > Your kernel may have been built without NUMA support.\r\n>> > > 2024-03-11 10:27:48.688059: I\r\n>> > external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could\r\n>> not\r\n>> > open file to read NUMA node:\r\n>> /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n>> > > Your kernel may have been built without NUMA support.\r\n>> > > 2024-03-11 10:27:48.688111: I\r\n>> > external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could\r\n>> not\r\n>> > open file to read NUMA node:\r\n>> /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n>> > > Your kernel may have been built without NUMA support.\r\n>> > > [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n>> > >\r\n>> > >\r\n>> > > —\r\n>> > > Reply to this email directly, view it on GitHub\r\n>> > > <\r\n>> >\r\n>> https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1987970615>,\r\n>>\r\n>> >\r\n>> > > or unsubscribe\r\n>> > > <\r\n>> >\r\n>> https://github.com/notifications/unsubscribe-auth/AAGK34PV2IIF5FUZ73EPKOTYXV2SZAVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBXHE3TANRRGU>\r\n>>\r\n>> >\r\n>> > > .\r\n>> > > You are receiving this because you were mentioned.Message ID:\r\n>> > > ***@***.***>\r\n>> > >\r\n>> >\r\n>> > —\r\n>> > Reply to this email directly, view it on GitHub\r\n>> > <\r\n>> https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1988394624>,\r\n>>\r\n>> > or unsubscribe\r\n>> > <\r\n>> https://github.com/notifications/unsubscribe-auth/AZRPJAGFJU5ZGBGHOSUK6DTYXWTU3AVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBYGM4TINRSGQ>\r\n>>\r\n>> > .\r\n>> > You are receiving this because you commented.Message ID:\r\n>> > ***@***.***>\r\n>> >\r\n>>\r\n>> —\r\n>> Reply to this email directly, view it on GitHub\r\n>> <https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1988630226>,\r\n>> or unsubscribe\r\n>> <https://github.com/notifications/unsubscribe-auth/AAGK34PLSBZFKQ5AQKIZ7X3YXXAMRAVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBYGYZTAMRSGY>\r\n>> .\r\n>> You are receiving this because you were mentioned.Message ID:\r\n>> ***@***.***>\r\n>>\r\n>\r\n",
"It's the python in the environment that I installed tensorflow[and-cuda]\r\nthere.\r\n\r\nOn Mon, Mar 11, 2024, 9:30 a.m. Juan E. Vargas ***@***.***>\r\nwrote:\r\n\r\n> Hi Shayan Shahrokhi\r\n>\r\n> Thank you for your suggestion (adding the location of the site-packages. I\r\n> hope you would not mind if I ask :\r\n>\r\n> I saw that in your suggestion the name python 3.12 is listed. Is that the\r\n> version of python that you used to test TF 2.16.1 compatibility with CUDA?\r\n>\r\n> Thank you, Juan\r\n>\r\n>\r\n>\r\n> On Mon, Mar 11, 2024 at 11:01 AM Juan Vargas ***@***.***> wrote:\r\n>\r\n> > will try that and will let you know. Thank you for the suggestion. Juan\r\n> >\r\n> >\r\n> > On Mon, Mar 11, 2024 at 10:52 AM Shayan Shahrokhi <\r\n> > ***@***.***> wrote:\r\n> >\r\n> >> It's just tensorflow can't see the Cuda libraries.\r\n> >>\r\n> >> Instal tensorflow[and-cuda] and add this to your .bashrc or conda\r\n> >> activation script\r\n> >>\r\n> >> NVIDIA_PACKAGE_DIR=\"$CONDA_PREFIX/lib/python3.12/site-packages/nvidia\"\r\n> >>\r\n> >> for dir in $NVIDIA_PACKAGE_DIR/*; do\r\n> >> if [ -d \"$dir/lib\" ]; then\r\n> >> export LD_LIBRARY_PATH=\"$dir/lib:$LD_LIBRARY_PATH\"\r\n> >> fi\r\n> >> done\r\n> >>\r\n> >>\r\n> >>\r\n> >> On Mon, Mar 11, 2024, 7:04 a.m. Juan E. Vargas ***@***.***>\r\n> >> wrote:\r\n> >>\r\n> >> > Hi Krzysztof\r\n> >> >\r\n> >> > I visited the site\r\n> >> >\r\n> >> >\r\n> >>\r\n> https://developer.nvidia.com/rdp/cudnn-archive?source=post_page-----bfbeb77e7c89--------------------------------\r\n> >> >\r\n> >> > where I found an entry listed as \" Local Installer for UBuntu22.04\r\n> >> > x86_64(Deb)\" which I downloaded.\r\n> >> > Unfortunately what I got is a package named\r\n> >> > \"cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb\"\r\n> >> > which is not the same as the name you suggest in your message, which\r\n> is\r\n> >> \"\r\n> >> > libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\"\r\n> >> >\r\n> >> > I assume what you meant is to get the libcudnn8_8.9.7.29*amd64.deb\r\n> and\r\n> >> > the cuda12.2_amd64.deb separately and install both.\r\n> >> >\r\n> >> > I have CUDA 12.4. I will not go back to trying to make TF 2.16.1 work\r\n> >> with\r\n> >> > older versions of CUDA (12.2 or 12.3) because sooner or later\r\n> >> > the TF team will have to produce a version with the updated version\r\n> of\r\n> >> > CUDA. IMHO, rather than us wasting time going back in versions,\r\n> >> > the TF beak should invest time going forward to update TF to the\r\n> >> current\r\n> >> > CUDA version.\r\n> >> >\r\n> >> > Thank you, Juan\r\n> >> >\r\n> >> >\r\n> >> > On Mon, Mar 11, 2024 at 5:30 AM Krzysztof Radzikowski <\r\n> >> > ***@***.***> wrote:\r\n> >> >\r\n> >> > > got it work :) first\r\n> >> > >\r\n> >> > >\r\n> >> >\r\n> >>\r\n> https://developer.nvidia.com/rdp/cudnn-archive?source=post_page-----bfbeb77e7c89--------------------------------\r\n> >> > >\r\n> >> > > then download Local Installer for Ubuntu22.04 x86_64 (Deb)\r\n> >> > > <\r\n> >> >\r\n> >>\r\n> https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.7/local_installers/12.x/cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb/>\r\n>\r\n> >>\r\n> >> >\r\n> >> > >\r\n> >> > > unpack and install libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\r\n> >> > >\r\n> >> > > `\r\n> >> > >\r\n> >> > > sudo dpkg -i libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb\r\n> >> > > Selecting previously unselected package libcudnn8.\r\n> >> > > (Reading database ... 47318 files and directories currently\r\n> >> installed.)\r\n> >> > > Preparing to unpack libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb ...\r\n> >> > > Unpacking libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\n> >> > > Setting up libcudnn8 (8.9.7.29-1+cuda12.2) ...\r\n> >> > >\r\n> >> > > `\r\n> >> > >\r\n> >> > > python3 -c \"import tensorflow as tf;\r\n> >> > print(tf.config.list_physical_devices('GPU'))\"\r\n> >> > >\r\n> >> > >\r\n> >> > > 2024-03-11 10:27:47.879686: I tensorflow/core/util/port.cc:113]\r\n> >> oneDNN\r\n> >> > custom operations are on. You may see slightly different numerical\r\n> >> results\r\n> >> > due to floating-point round-off errors from different computation\r\n> >> orders.\r\n> >> > To turn them off, set the environment variable\r\n> >> `TF_ENABLE_ONEDNN_OPTS=0`.\r\n> >> > > 2024-03-11 10:27:47.909157: I\r\n> >> > tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow\r\n> >> binary\r\n> >> > is optimized to use available CPU instructions in\r\n> performance-critical\r\n> >> > operations.\r\n> >> > > To enable the following instructions: AVX2 AVX_VNNI FMA, in other\r\n> >> > operations, rebuild TensorFlow with the appropriate compiler flags.\r\n> >> > > 2024-03-11 10:27:48.316717: W\r\n> >> > tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning:\r\n> >> Could\r\n> >> > not find TensorRT\r\n> >> > > 2024-03-11 10:27:48.664469: I\r\n> >> > external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984]\r\n> could\r\n> >> not\r\n> >> > open file to read NUMA node:\r\n> >> /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> >> > > Your kernel may have been built without NUMA support.\r\n> >> > > 2024-03-11 10:27:48.688059: I\r\n> >> > external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984]\r\n> could\r\n> >> not\r\n> >> > open file to read NUMA node:\r\n> >> /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> >> > > Your kernel may have been built without NUMA support.\r\n> >> > > 2024-03-11 10:27:48.688111: I\r\n> >> > external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984]\r\n> could\r\n> >> not\r\n> >> > open file to read NUMA node:\r\n> >> /sys/bus/pci/devices/0000:01:00.0/numa_node\r\n> >> > > Your kernel may have been built without NUMA support.\r\n> >> > > [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n> >> > >\r\n> >> > >\r\n> >> > > —\r\n> >> > > Reply to this email directly, view it on GitHub\r\n> >> > > <\r\n> >> >\r\n> >>\r\n> https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1987970615>,\r\n>\r\n> >>\r\n> >> >\r\n> >> > > or unsubscribe\r\n> >> > > <\r\n> >> >\r\n> >>\r\n> https://github.com/notifications/unsubscribe-auth/AAGK34PV2IIF5FUZ73EPKOTYXV2SZAVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBXHE3TANRRGU>\r\n>\r\n> >>\r\n> >> >\r\n> >> > > .\r\n> >> > > You are receiving this because you were mentioned.Message ID:\r\n> >> > > ***@***.***>\r\n> >> > >\r\n> >> >\r\n> >> > —\r\n> >> > Reply to this email directly, view it on GitHub\r\n> >> > <\r\n> >>\r\n> https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1988394624>,\r\n>\r\n> >>\r\n> >> > or unsubscribe\r\n> >> > <\r\n> >>\r\n> https://github.com/notifications/unsubscribe-auth/AZRPJAGFJU5ZGBGHOSUK6DTYXWTU3AVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBYGM4TINRSGQ>\r\n>\r\n> >>\r\n> >> > .\r\n> >> > You are receiving this because you commented.Message ID:\r\n> >> > ***@***.***>\r\n> >> >\r\n> >>\r\n> >> —\r\n> >> Reply to this email directly, view it on GitHub\r\n> >> <\r\n> https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1988630226>,\r\n>\r\n> >> or unsubscribe\r\n> >> <\r\n> https://github.com/notifications/unsubscribe-auth/AAGK34PLSBZFKQ5AQKIZ7X3YXXAMRAVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBYGYZTAMRSGY>\r\n>\r\n> >> .\r\n> >> You are receiving this because you were mentioned.Message ID:\r\n> >> ***@***.***>\r\n> >>\r\n> >\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1988718350>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AZRPJACFR4YH7UCXMOEH7MTYXXEXVAVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOBYG4YTQMZVGA>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"\r\nhttps://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ you can get .deb file there directrly\r\n",
"Thanks @sh-shahrokhi. I thought it was path related. Modified slightly to make it python version independent if you put it in your conda environment activation (`[environment]/etc/activate.d/env_vars.sh`).\r\n\r\n```\r\nNVIDIA_DIR=$(dirname $(dirname $(python -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\")))\r\nfor dir in $NVIDIA_DIR/*; do\r\n if [ -d \"$dir/lib\" ]; then\r\n export LD_LIBRARY_PATH=\"$dir/lib:$LD_LIBRARY_PATH\"\r\n fi\r\ndone\r\n```\r\nThis is not a resolution as this post install step should not be necessary.\r\n\r\n`W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT`\r\n\r\nI can't seem to do similar tricks to resolve the TensorRT issues when installed similarly into the conda environment. Any ideas?",
"> Thanks @sh-shahrokhi. I thought it was path related. Modified slightly to make it python version independent if you put it in your conda environment activation (`[environment]/etc/activate.d/env_vars.sh`).\r\n> \r\n> ```\r\n> NVIDIA_DIR=$(dirname $(dirname $(python -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\")))\r\n> for dir in $NVIDIA_DIR/*; do\r\n> if [ -d \"$dir/lib\" ]; then\r\n> export LD_LIBRARY_PATH=\"$dir/lib:$LD_LIBRARY_PATH\"\r\n> fi\r\n> done\r\n> ```\r\n> \r\n> This is not a resolution as this post install step should not be necessary.\r\n> \r\n> `W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT`\r\n> \r\n> I can't seem to do similar tricks to resolve the TensorRT issues when installed similarly into the conda environment. Any ideas?\r\n\r\nI don't actually use TensorRT, but I would check if the required .so file for it is visible to tensorflow. Maybe I would need to find the name of required file in tensorflow source code.\r\n\r\nThis actually doesn't change the fact that the new tensorflow version should be tested by google team before release, or the bugs should be fixed. It seems they only care about having a working docker image, not anything else.\r\n",
"I have given up on TensorRT. I guess I won't be using it either.\r\n\r\n> This actually doesn't change the fact that the new tensorflow version should be tested by google team before release, or the bugs should be fixed. It seems they only care about having a working docker image, not anything else.\r\n\r\nAgreed. Installing TF has always been hit or miss and it seems that in the many years since I last used TF that hasn't changed one bit. \r\n",
"Well, I wasted 8hr of my Sunday on this setting up another pc from scratch. Before reverting to the old version. Now looking to move off tensor flow.",
"In general, we used to test RC versions before release. For example, we used to have RC0, RC1 and RC2 for TF 2.9. This gave people and downstream teams enough time to test and report issues.\r\n\r\nIt seems that 2.16.1 only had an RC0 (for 2.16.0).\r\n\r\nThe release process is (was?) like this:\r\n- cut the release branch (e.g., `r2.17`)\r\n- immediately trigger the release pipeline. This would create a few PRs to update version numbers, release notes, but after this step RC0 should be as close as possible to the version on master branch at the time the release branch has been cut. There should not be any code changes to the release branch at this point (except to maybe cherrypick fixes from master from hard bugs caused by cutting the branch at a wrong commit)\r\n- have at least a week of testing for downstream teams to test RC0\r\n- get fixes to discovered bugs landed on master, cherrypick them to release branch, after they are already tested on nightly releases\r\n- trigger RC1 pipeline. Again, no other code changes should occur now, except to fix bugs discovered during building\r\n- wait a week for downstream teams to test. If there are bugs, repeat the steps above for another RC, otherwise repeat the steps above for the final version.\r\n\r\nOverall, this process would take `number_of_RCs + 1` weeks with a possibility of a few more weeks of delay.\r\n\r\nHowever, for 2.16 release, although the branch was cut on Feb 8th, there has been only one RC. Most likely issues can be solved by a patch release",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63362\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63362\">No</a>\n",
"I am closing this (unresolved issue) because I am told by the Keras/TF team that the issue is related to TF. ",
"So where is the resolution to this quite annoying installation issue being tracked? \r\n\r\nIt’s kind of ridiculous that a fresh install using the official method results in a system that can’t access the GPUs out of the box. As others have noted this sort of dealbreaker when setting up a new machine pushes people away from TF. ",
"I closed the issue because I was asked to do so by a TF/Keras team member,\nfor the reasons that I stated in the closing comment. Just like you (Leigh)\nI am disappointed, and Idecided not to waste more time. There are strong\nreasons for some of us (users) to prefer using our own hardware. I hope the\nTF/keras people get that message. Thanks, Juan\n\n\n> Message ID: ***@***.***>\n>\n",
"You were asked to close the issue on keras, because it is not a keras issue. This is tensorflow, where proper detection of the cuda libs is definitely still an issue. Please reopen. ",
"I already opened a similar issue in TF.\r\n\r\nOn Tue, Mar 12, 2024 at 9:20 AM Sam Levang ***@***.***> wrote:\r\n\r\n> You were asked to close the issue on keras, because it is not a keras\r\n> issue. This is tensorflow, where proper detection of the cuda libs is\r\n> definitely still an issue. Please reopen.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1991637460>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AAGK34OEUIJZCSKQOGZCGQTYX36J7AVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOJRGYZTONBWGA>\r\n> .\r\n> You are receiving this because you modified the open/close state.Message\r\n> ID: ***@***.***>\r\n>\r\n",
"Again, **you are on the tensorflow repo**. You closed the wrong issue.",
"Oh, sorry about that!\r\n\r\nOn Tue, Mar 12, 2024 at 9:46 AM Sam Levang ***@***.***> wrote:\r\n\r\n> Again, *you are on the tensorflow repo*. You closed the wrong issue.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1991690836>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AAGK34K46DAULWCUUNQHYKDYX4BLJAVCNFSM6AAAAABEOPWBC2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSOJRGY4TAOBTGY>\r\n> .\r\n> You are receiving this because you modified the open/close state.Message\r\n> ID: ***@***.***>\r\n>\r\n",
"Issue reopened",
"It is really frustrating indeed. The silly thing is that with JAX as the Keras backend GPU is utilized smoothly (still my RTX3060 GPU is not in the list of physical devices that TensorFlow's framework is aware of (!) ).. I guess we cannot do something else but wait until the next version release... Thank you Mr. Juan for raising the issue here."
] | 2024-03-10T00:17:36 | 2024-06-10T14:25:22 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
TF 2.16.1
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04.4 LTS
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.4
### GPU model and memory
_No response_
### Current behavior?
I created a python venv in which I installed TF 2.16.1 following your instructions: pip install tensorflow
When I run python, import tf, and issue tf.config.list_physical_devices('GPU')
I get an empty list [ ]
I created another python venv, installed TF 2.16.1, only this time with the instructions:
python3 -m pip install tensorflow[and-cuda]
When I run that version, import tensorflow as tf, and issue
tf.config.list_physical_devices('GPU')
I also get an empty list.
BTW, I have no problems running on my box TF 2.15.1 with GPUs. Julia also works just fine with GPUs and so does PyTorch.
the
### Standalone code to reproduce the issue
```shell
Python 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
2024-03-09 19:15:45.018171: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-09 19:15:50.412646: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
>>> tf.__version__
'2.16.1'
tf.config.list_physical_devices('GPU')
2024-03-09 19:16:28.923792: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-09 19:16:29.078379: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[]
>>>
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5pJyyb
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Replace `RemoteTensorHandle` with `TensorProto` for scalars in an `EnqueueRequest` except for `DT_RESOURCE`
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[] | 2024-03-09T20:18:30 | 2024-04-10T19:12:52 | 2024-04-10T19:12:49 |
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Replace `RemoteTensorHandle` with `TensorProto` for scalars in an `EnqueueRequest` except for `DT_RESOURCE`
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tensorflow 2.16.1 build error: Compiling xla/service/cpu/onednn_matmul.cc failed
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[
"@spamming4 Please ensure you have OneDNN installed on your system. Kindly refer to the official OneDNN installation guide for your operating system: https://onnxruntime.ai/docs/execution-providers/oneDNN-ExecutionProvider.html\r\nalso verify that the installed OneDNN version is compatible with TensorFlow 2.16.1. \r\nThank you!",
"> Please ensure you have OneDNN installed on your system. Kindly refer to the official OneDNN installation guide for your operating system: https://onnxruntime.ai/docs/execution-providers/oneDNN-ExecutionProvider.html\r\n\r\nDid ChatGPT write this?\r\n\r\n+@agramesh1",
"@spamming4 the TF build option --config=mkl is part of the Intel tensorFlow distribution and no longer supported as of TF 2.15 (see https://github.com/Intel-tensorflow/tensorflow/releases/tag/v2.14.0) as all the oneDNN optimization as currently available with the official build. You should be able to compile TF without using --config=mkl .\r\n",
"So if I understand correctly, we build without `--config=mkl` and install `intel-extension-for-tensorflow` alongside tf?",
"@sirfz, oneDNN support is enabled by default on Windows in Tensorflow [v2.15](https://github.com/tensorflow/tensorflow/releases/tag/v2.15.0) and newer. You don't need to do anything special. Just grab Tensorflow build from whatever channel you like or build from source.",
"> @spamming4 Please ensure you have OneDNN installed on your system. Kindly refer to the official OneDNN installation guide for your operating system: https://onnxruntime.ai/docs/execution-providers/oneDNN-ExecutionProvider.html also verify that the installed OneDNN version is compatible with TensorFlow 2.16.1. Thank you!\r\n\r\n[sushreebarsa](https://github.com/sushreebarsa), thank you, that helped to resolve the issue.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63360\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63360\">No</a>\n",
"I ran into the same error and found the answers here misleading.\r\n\r\nI can confirm that the prebuilt package (`pip install tensorflow==2.16.1`) gives competitive performance to anything I was able to build from source. Had to set `TF_ENABLE_ONEDNN_OPTS=1` for that.\r\n\r\nBut to clarify:\r\n> the TF build option --config=mkl is part of the Intel tensorFlow distribution and no longer supported as of TF 2.15\r\n\r\nI build the official TF 2.16.1 with and without `--config=mkl` and saw a clear difference in performance (even with `with_xla_support=false`which I had to set to get to compile). So this statement holds only for [intel-tensorflow](https://github.com/Intel-tensorflow/tensorflow)? Even in intel-tensorflow the option still seems to exist in .bazelrc?\r\n\r\n> You should be able to compile TF without using --config=mkl .\r\n\r\nTrue, that \"solves\" the original compilation error (which is about official TF 2.16.1) but results in worse performance.\r\n\r\n> oneDNN support is enabled by default on Windows in Tensorflow [v2.15](https://github.com/tensorflow/tensorflow/releases/tag/v2.15.0) and newer\r\n\r\nWho asked about windows? 😉 On Ubuntu I had to set `TF_ENABLE_ONEDNN_OPTS=1` as I said, not sure if that's the case for everyone? But yes, other than that works out of the box. 🙌 \r\n\r\n> sushreebarsa, thank you, that helped to resolve the issue.\r\n\r\nReally? 😅 Build instructions for ONNX? There are no necessary steps mentioned regarding oneDNN on https://www.tensorflow.org/install/source when using `--config==mkl`. Are there any?\r\n\r\n---\r\n\r\nIn any case, the original error\r\n```\r\nerror: cannot convert ‘std::unique_ptr<tsl::OneDnnThreadPool>::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’\r\n```\r\nis not solved. And the status is \"won't fix\"?",
"I am not working on XLA:CPU. @penpornk maybe you can comment on the compile error reported here?"
] | 2024-03-09T20:04:58 | 2024-06-03T11:54:55 | 2024-03-16T14:03:17 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11.8
### Bazel version
6.5.0
### GCC/compiler version
11.4.0
### CUDA/cuDNN version
12.4/9.0.0.312
### GPU model and memory
NVIDIA GeForce 940MX
### Current behavior?
INFO: Reading 'startup' options from ~/Documents/dev/git/tensorflow/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=211
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.bazelrc:
'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env PYTHON_LIB_PATH=~/Documents/dev/programs/miniconda3/envs/tf/lib/python3.11/site-packages --python_path=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env CUDA_TOOLKIT_PATH=/usr/local/cuda-12.3 --action_env TF_CUDA_COMPUTE_CAPABILITIES=5.0 --action_env LD_LIBRARY_PATH=/usr/lib/libreoffice/program:/usr/local/cuda/targets/x86_64-linux/lib:/usr/lib/x86_64-linux-gnu --action_env GCC_HOST_COMPILER_PATH=/usr/bin/x86_64-linux-gnu-gcc-11 --config=cuda
INFO: Found applicable config definition build:short_logs in file ~/Documents/dev/git/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:cuda in file ~/Documents/dev/git/tensorflow/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
INFO: Found applicable config definition build:mkl in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=build_with_mkl=true --define=enable_mkl=true --define=tensorflow_mkldnn_contraction_kernel=0 --define=build_with_openmp=true -c opt
INFO: Found applicable config definition build:opt in file ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare
INFO: Found applicable config definition build:linux in file ~/Documents/dev/git/tensorflow/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
INFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (711 packages loaded, 51601 targets configured).
INFO: Found 1 target...
ERROR: ~/.cache/bazel/_bazel_vyepishov/cf67b2b2e967476eb2b1ee98e33ab5bd/external/local_xla/xla/service/cpu/BUILD:1638:11: Compiling xla/service/cpu/onednn_matmul.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target @local_xla//xla/service/cpu:onednn_matmul) external/local_config_cuda/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc -MD -MF bazel-out/k8-opt/bin/external/local_xla/xla/service/cpu/_objs/onednn_matmul/onednn_matmul.pic.d ... (remaining 229 arguments skipped)
In file included from external/local_xla/xla/shape.h:28,
from external/local_xla/xla/service/cpu/onednn_matmul.h:21,
from external/local_xla/xla/service/cpu/onednn_matmul.cc:18:
external/local_xla/xla/layout.h:377:18: warning: ‘xla::Layout::DimInfo::dim_level_type’ is too small to hold all values of ‘enum xla::DimLevelType’
377 | DimLevelType dim_level_type : 6;
| ^~~~~~~~~~~~~~
external/local_xla/xla/layout.h:389:17: warning: ‘xla::Layout::index_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’
389 | PrimitiveType index_primitive_type_ : 8;
| ^~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/layout.h:390:17: warning: ‘xla::Layout::pointer_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’
390 | PrimitiveType pointer_primitive_type_ : 8;
| ^~~~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMul(void*, void**)’:
external/local_xla/xla/service/cpu/onednn_matmul.cc:186:68: error: cannot convert ‘std::unique_ptr<tsl::OneDnnThreadPool>::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’
186 | auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get());
| ~~~~~~~~~~~~~~~^~
| |
| std::unique_ptr<tsl::OneDnnThreadPool>::pointer {aka tsl::OneDnnThreadPool*}
external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note: initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’
148 | dnnl::threadpool_interop::threadpool_iface* thread_pool) {
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~
external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMulReorder(void*, void**)’:
external/local_xla/xla/service/cpu/onednn_matmul.cc:322:68: error: cannot convert ‘std::unique_ptr<tsl::OneDnnThreadPool>::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’
322 | auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get());
| ~~~~~~~~~~~~~~~^~
| |
| std::unique_ptr<tsl::OneDnnThreadPool>::pointer {aka tsl::OneDnnThreadPool*}
external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note: initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’
148 | dnnl::threadpool_interop::threadpool_iface* thread_pool) {
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 16142.186s, Critical Path: 328.40s
INFO: 25824 processes: 8831 internal, 16993 local.
FAILED: Build did NOT complete successfully
### Standalone code to reproduce the issue
```shell
bazel build --config=mkl --config=opt //tensorflow/tools/pip_package:build_pip_package
```
### Relevant log output
```shell
INFO: Reading 'startup' options from ~/Documents/dev/git/tensorflow/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=211
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.bazelrc:
'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env PYTHON_LIB_PATH=~/Documents/dev/programs/miniconda3/envs/tf/lib/python3.11/site-packages --python_path=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env CUDA_TOOLKIT_PATH=/usr/local/cuda-12.3 --action_env TF_CUDA_COMPUTE_CAPABILITIES=5.0 --action_env LD_LIBRARY_PATH=/usr/lib/libreoffice/program:/usr/local/cuda/targets/x86_64-linux/lib:/usr/lib/x86_64-linux-gnu --action_env GCC_HOST_COMPILER_PATH=/usr/bin/x86_64-linux-gnu-gcc-11 --config=cuda
INFO: Found applicable config definition build:short_logs in file ~/Documents/dev/git/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:cuda in file ~/Documents/dev/git/tensorflow/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
INFO: Found applicable config definition build:mkl in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=build_with_mkl=true --define=enable_mkl=true --define=tensorflow_mkldnn_contraction_kernel=0 --define=build_with_openmp=true -c opt
INFO: Found applicable config definition build:opt in file ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare
INFO: Found applicable config definition build:linux in file ~/Documents/dev/git/tensorflow/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
INFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (711 packages loaded, 51601 targets configured).
INFO: Found 1 target...
ERROR: ~/.cache/bazel/_bazel_vyepishov/cf67b2b2e967476eb2b1ee98e33ab5bd/external/local_xla/xla/service/cpu/BUILD:1638:11: Compiling xla/service/cpu/onednn_matmul.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target @local_xla//xla/service/cpu:onednn_matmul) external/local_config_cuda/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc -MD -MF bazel-out/k8-opt/bin/external/local_xla/xla/service/cpu/_objs/onednn_matmul/onednn_matmul.pic.d ... (remaining 229 arguments skipped)
In file included from external/local_xla/xla/shape.h:28,
from external/local_xla/xla/service/cpu/onednn_matmul.h:21,
from external/local_xla/xla/service/cpu/onednn_matmul.cc:18:
external/local_xla/xla/layout.h:377:18: warning: ‘xla::Layout::DimInfo::dim_level_type’ is too small to hold all values of ‘enum xla::DimLevelType’
377 | DimLevelType dim_level_type : 6;
| ^~~~~~~~~~~~~~
external/local_xla/xla/layout.h:389:17: warning: ‘xla::Layout::index_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’
389 | PrimitiveType index_primitive_type_ : 8;
| ^~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/layout.h:390:17: warning: ‘xla::Layout::pointer_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’
390 | PrimitiveType pointer_primitive_type_ : 8;
| ^~~~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMul(void*, void**)’:
external/local_xla/xla/service/cpu/onednn_matmul.cc:186:68: error: cannot convert ‘std::unique_ptr<tsl::OneDnnThreadPool>::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’
186 | auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get());
| ~~~~~~~~~~~~~~~^~
| |
| std::unique_ptr<tsl::OneDnnThreadPool>::pointer {aka tsl::OneDnnThreadPool*}
external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note: initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’
148 | dnnl::threadpool_interop::threadpool_iface* thread_pool) {
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~
external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMulReorder(void*, void**)’:
external/local_xla/xla/service/cpu/onednn_matmul.cc:322:68: error: cannot convert ‘std::unique_ptr<tsl::OneDnnThreadPool>::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’
322 | auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get());
| ~~~~~~~~~~~~~~~^~
| |
| std::unique_ptr<tsl::OneDnnThreadPool>::pointer {aka tsl::OneDnnThreadPool*}
external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note: initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’
148 | dnnl::threadpool_interop::threadpool_iface* thread_pool) {
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 16142.186s, Critical Path: 328.40s
INFO: 25824 processes: 8831 internal, 16993 local.
FAILED: Build did NOT complete successfully
```
|
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PR_kwDOArmXAs5pJxg1
| 63,359 |
Fix SegFault in Python InterpreterWrapper
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[
"Hi @vamsimanchala Can you please review this PR ? Thank you!",
"Hi @vamsimanchala Can you please review this PR ? Thank you!"
] | 2024-03-09T19:57:47 | 2024-06-07T16:56:35 | null |
NONE
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If `InterpreterWrapper::TensorSparsityParameters` encounters Tensors which do not have a `block_map`, a `nullptr` is dereferenced causing AccViol/SegFault.
Add a check for `nullptr`.
Attempts to fix #62058
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PR_kwDOArmXAs5pJwmL
| 63,358 |
Force an extra step from pred to s32 before then converting to f32 as that can fail on TGP
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Force an extra step from pred to s32 before then converting to f32 as that can fail on TGP
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10066 from ROCm:ci_profiler_fixes 50d23258e024e75d640a241775a4a6dbfbfb5d51
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[JAX] Temporarily readd CompiledFunction to fix some downstream breakage.
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I_kwDOArmXAs6Bx0Ja
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Build error related to XLA and absl
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[
"Note: this only occurs when I build with CUDA support. I doesn't occur if I build for the CPU.",
"Hi **@adamjstewart** ,\r\n\r\nCould you update the CUDA version, For tensorflow-2.16.1 we need to update CUDA 12.3. Please refer [source](https://www.tensorflow.org/install/source) here.\r\nCan you please execute your code using Latest CUDA Version (12.3) and let us know if the issue still persists?\r\n\r\nThank you!",
"Thanks, that seems to have done the trick!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63356\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63356\">No</a>\n"
] | 2024-03-09T17:20:29 | 2024-03-11T12:54:16 | 2024-03-11T12:54:13 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
11.4.0
### CUDA/cuDNN version
11.8.0/8.9.7.29
### GPU model and memory
_No response_
### Current behavior?
When building TF from source using the Spack package manager, I see the following build failure:
```
ERROR: /tmp/spackkiy_sjk0/dfa266778fb055fec5b77ad2acb73759/external/local_xla/xla/service/gpu/kernels/BUILD:157:13: Compiling xla/service/gpu/kernels/topk_kernel_bfloat16.cu.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target @local_xla//xla/service/gpu/kernels:topk_kernel_cuda)
...
external/com_google_absl/absl/strings/internal/str_format/bind.h: In constructor ‘absl::lts_20230802::str_format_internal::FormatSpecTemplate<Args>::FormatSpecTemplate(const absl::lts_20230802::str_format_internal::ExtendedParsedFormat<absl::lts_20230802::FormatConversionCharSet(C)...>&)’:
external/com_google_absl/absl/strings/internal/str_format/bind.h:172:1: error: parse error in template argument list
172 | CheckArity<sizeof...(C), sizeof...(Args)>();
| ^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
external/com_google_absl/absl/strings/internal/str_format/bind.h:172:63: error: expected ‘;’ before ‘)’ token
172 | CheckArity<sizeof...(C), sizeof...(Args)>();
| ^
external/com_google_absl/absl/strings/internal/str_format/bind.h:173:147: error: template argument 1 is invalid
173 | CheckMatches<C...>(absl::make_index_sequence<sizeof...(C)>{});
| ^
external/com_google_absl/absl/strings/internal/str_format/bind.h:173:151: error: expected primary-expression before ‘{’ token
173 | CheckMatches<C...>(absl::make_index_sequence<sizeof...(C)>{});
| ^
external/com_google_absl/absl/strings/internal/str_format/bind.h:173:151: error: expected ‘;’ before ‘{’ token
external/com_google_absl/absl/strings/internal/str_format/bind.h:173:153: error: expected primary-expression before ‘)’ token
173 | CheckMatches<C...>(absl::make_index_sequence<sizeof...(C)>{});
| ^
Target //tensorflow/tools/pip_package:build_pip_package failed to build
INFO: Elapsed time: 1238.631s, Critical Path: 57.51s
INFO: 17066 processes: 6004 internal, 11062 local.
FAILED: Build did NOT complete successfully
```
### Standalone code to reproduce the issue
See the below build log for steps to reproduce the issue.
### Relevant log output
* [build log](https://github.com/tensorflow/tensorflow/files/14547197/spack-build-out.txt)
* [build env](https://github.com/tensorflow/tensorflow/files/14547196/spack-build-env-mods.txt)
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core dumped with tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel
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[
"@vwrewsge,\r\nI was able to reproduce the issue on tf-nightly and tensorflow v2.15. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/61d100d42a54030ba48c443aa6f0449f/untitled1749.ipynb). Could you please allow some time to deep dive into the issue and will provide the resolution for the same. Thank you!"
] | 2024-03-09T15:03:18 | 2024-03-15T13:07:44 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
core dumped error with specific input parameters.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# Generate input data
input_data = tf.constant([[1.5, 2.5, 3.5], [4.5, 5.5, 6.5]])
# Define min and max values per channel
min_per_channel = tf.constant([1.0, 2.0, 3.0])
max_per_channel = tf.constant([2.0, 3.0, 4.0])
# Invoke tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel with inputs as 0-dimensional tensor and max as a 1x3 tensor
quantized_output = tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(inputs=tf.constant(0.0), min=min_per_channel, max=max_per_channel, num_bits=8, narrow_range=False)
# Print the quantized output
print(quantized_output)
```
### Relevant log output
```shell
2024-03-09 15:02:07.858055: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)
```
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[
"Hi @vwrewsge ,\r\n\r\nThanks for reporting.\r\n\r\nThis got fixed recently with the PR #63105 which already merged with master. Checked with tf-nightly and its raising exception.Refer [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/81dd320ec588dd5ca28c020c14b6bab1/63354.ipynb).\r\n\r\n",
"Thanks for your reply!",
"Hi @vwrewsge ,\r\n\r\nCould we mark it as closed if resolved for you? Thanks!",
"Thanks a lot",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63354\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63354\">No</a>\n"
] | 2024-03-09T14:57:17 | 2024-03-12T13:19:58 | 2024-03-12T13:19:55 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
core dumped error with specific input parameters.
### Standalone code to reproduce the issue
1. The code of `tf.raw_ops.DrawBoundingBoxes`:
```shell
import tensorflow as tf
import numpy as np
# Generate input data
batch_size = 1
image_height = 100
image_width = 100
num_channels = 3
num_boxes = 2
images = np.random.rand(image_height, image_width, num_channels).astype(np.float32) # Remove the batch dimension
boxes = np.random.rand(batch_size, num_boxes, 4).astype(np.float32)
# Invoke tf.raw_ops.DrawBoundingBoxes with a zero-dimensional tensor for images
drawn_images = tf.raw_ops.DrawBoundingBoxes(images=tf.convert_to_tensor(images),
boxes=tf.convert_to_tensor(boxes))
# Print the result
print(drawn_images)
```
2. The code of `tf.raw_ops.DrawBoundingBoxesV2`:
```
import tensorflow as tf
import numpy as np
# Generate input data
image_height = 100
image_width = 100
num_channels = 3
num_boxes = 2
images = tf.random.uniform((image_height, image_width, num_channels)) # Change the shape to satisfy the requirement of a zero-dimensional tensor
boxes = tf.random.uniform((1, num_boxes, 4))
colors = tf.constant([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]) # Define colors for each bounding box
# Invoke tf.raw_ops.DrawBoundingBoxesV2
output_images = tf.raw_ops.DrawBoundingBoxesV2(images=images, boxes=boxes, colors=colors)
# Print the output images
print(output_images)
```
### Relevant log output
```shell
2024-03-09 14:55:53.834849: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (3 vs. 3)
Aborted (core dumped)
```
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I_kwDOArmXAs6BxkW2
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Aborted (core dumped) with tf.raw_ops.AvgPoolGrad
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[
"@sachinprasadhs I was able to replicate the error reported [here](https://colab.research.google.com/gist/sushreebarsa/fa1b2962c80219519380bfebc99f9879/63353.ipynb). Thank you!"
] | 2024-03-09T14:54:40 | 2024-03-13T09:37:27 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
core dumped error with specific input parameters.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# Generate input data
input_data = tf.random.normal([1, 28, 28, 3])
grad = tf.random.normal([1, 14, 14, 6]) # Change the number of channels in grad tensor
# Perform average pooling
result = tf.nn.avg_pool2d(input_data, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', data_format='NHWC')
# Compute gradient
grad_result = tf.raw_ops.AvgPoolGrad(orig_input_shape=tf.shape(input_data), grad=grad, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', data_format='NHWC')
print(grad_result)
```
### Relevant log output
```shell
free(): corrupted unsorted chunks
Aborted (core dumped)
```
|
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[
"Hi **@vwrewsge** ,\r\n\r\nI tried to run your code on colab using tf 2.15 and tf-nightly versions faced same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/6eee139887f6a9b45d5ff6bc233a4bd9/63352_2-15_nightly-v.ipynb) here for reference. We need to work on it, In few days i will provide a solution for it.\r\n\r\nThank you!"
] | 2024-03-09T14:50:26 | 2024-05-30T04:11:58 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Segmentation fault error with specific input parameters.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# Generate input data
input_data = tf.random.normal([1, 44100], dtype=tf.float32)
# Invoke tf.raw_ops.AudioSpectrogram with a negative window_size
spectrogram = tf.raw_ops.AudioSpectrogram(input=input_data, window_size=-1024, stride=64, magnitude_squared=False)
# Print the spectrogram
print(spectrogram)
```
### Relevant log output
```shell
Segmentation fault (core dumped)
```
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[
"@vwrewsge,\r\nI request you please take a look at this issue https://github.com/tensorflow/tensorflow/issues/46910 where a similar issue has been raised and it is still open. Could you please follow that issue, since it is already being tracked there? Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63351\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63351\">No</a>\n"
] | 2024-03-09T14:47:46 | 2024-03-27T01:47:15 | 2024-03-27T01:47:12 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
core dumped error with specific input parameters.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input_data = tf.constant(3.0)
min_per_channel = tf.constant(2.0)
max_per_channel = tf.constant(4.0)
quantized_data = tf.quantization.fake_quant_with_min_max_vars_per_channel(input_data, min_per_channel, max_per_channel)
print(quantized_data)
```
### Relevant log output
```shell
2024-03-09 14:43:28.826225: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)
```
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Introduce API for lazily allocating buffers in ToLiteral.
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Introduce API for lazily allocating buffers in ToLiteral.
This allows efficient implementation of runahead without using host memory.
Other backends can implement the behavior if/when necessary.
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The Op raw_ops.MatrixDiagV3 terminates due to checkfail when the input `diagonal` is of `rank=1` and `k[0] != k[1]`.
This happens at this line below which is trying to access `diagonal_shape.dim_size(-1)` when rank=1 . Currently API will not work with Rank=1 and k[0] != k[1] due to this check fail.
https://github.com/tensorflow/tensorflow/blob/e193d8ea7776ef5c6f5d769b6fb9c070213e737a/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L241
Proposed a fix for same.
Fixes #62984 .
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GPU not detected on WSL2
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"I've gotten the same problem but with python3.12. I found it was a problem with environment variables and finally fixed it. My solution is (if in pwsh):\r\n\r\n- Make sure cuda and cudnn are installed correctly.\r\n- Make sure `$Env:CUDA_PATH` is given correctly\r\n- Make sure `$Env:LD_LIBRARY_PATH` is given correctly\r\n- Make sure `$Env:TF_CUDA_PATHS` is given correctly\r\n- Use `pip install tensorflow[and-cuda]` instead of `pip install tensorflow`. Because the extensions, such as `nvidia-cuda-*` and `nvidia-cudnn-*` from pip(Pypi) are also needed.",
"@Zhaopudark thank you for sharing. what path shoud I set for those environment variables ?\r\nMake sure $Env:CUDA_PATH is given correctly\r\nMake sure $Env:LD_LIBRARY_PATH is given correctly\r\nMake sure $Env:TF_CUDA_PATHS is given correctly",
"> @Zhaopudark thank you for sharing. what path shoud I set for those environment variables ? Make sure $Env:CUDA_PATH is given correctly Make sure $Env:LD_LIBRARY_PATH is given correctly Make sure $Env:TF_CUDA_PATHS is given correctly\r\n\r\nThat depends on your specific installation method of cuda and cudnn. For me, since I use miniconda and [conda Installation](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#conda-installation), so my setting is as:\r\n```pwsh\r\n# pwsh\r\n# if `conda activate my_env `\r\n# an env variable `$Env:CONDA_PREFIX` will be `/home/your_name/miniconda3/envs/my_env `\r\n# So, the setting is as:\r\n$Env:CUDA_PATH = $Env:CONDA_PREFIX\r\n$Env:LD_LIBRARY_PATH=\"$Env:CONDA_PREFIX/lib\"\r\n$Env:TF_CUDA_PATHS=\"$Env:CONDA_PREFIX\"\r\n```\r\nActually, either [package manager installation](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#wsl) or [conda Installation](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#conda-installation), the `$Env:CUDA_PATH` should be such a root path where you can see cuda's installed directories, such as `./nvvm`, `./nvvm`, `./extras/Debugger/lib64/libcudacore.a` and so on. And, `$Env:TF_CUDA_PATHS` should be equal to `$Env:CUDA_PATH`.\r\n",
"> > @Zhaopudark thank you for sharing. what path shoud I set for those environment variables ? Make sure $Env:CUDA_PATH is given correctly Make sure $Env:LD_LIBRARY_PATH is given correctly Make sure $Env:TF_CUDA_PATHS is given correctly\r\n> \r\n> That depends on your specific installation method of cuda and cudnn. For me, since I use miniconda and [conda Installation](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#conda-installation), so my setting is as:\r\n> \r\n> ```powershell\r\n> # pwsh\r\n> # if `conda activate my_env `\r\n> # an env variable `$Env:CONDA_PREFIX` will be `/home/your_name/miniconda3/envs/my_env `\r\n> # So, the setting is as:\r\n> $Env:CUDA_PATH = $Env:CONDA_PREFIX\r\n> $Env:LD_LIBRARY_PATH=\"$Env:CONDA_PREFIX/lib\"\r\n> $Env:TF_CUDA_PATHS=\"$Env:CONDA_PREFIX\"\r\n> ```\r\n> \r\n> Actually, either [package manager installation](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#wsl) or [conda Installation](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#conda-installation), the `$Env:CUDA_PATH` should be such a root path where you can see cuda's installed directories, such as `./nvvm`, `./nvvm`, `./extras/Debugger/lib64/libcudacore.a` and so on. And, `$Env:TF_CUDA_PATHS` should be equal to `$Env:CUDA_PATH`.\r\n\r\nDoes your setup detect GPU with tensorflow 2.16.1? mine works with 2.15 but not this one.",
"@sh-shahrokhi Yes, since I use python3.12 and only tensorflow-2.16.1 that can be installed by pip. Maybe you should check your cuda and cudnn version that satisfied tensorflow-2.16.1's requirements. \r\n",
"@Zhaopudark Thank you for your comment.\r\n\r\nRegarding the cudnn and cuda version, I searched around and finally found a matrix [here](https://www.tensorflow.org/install/source#gpu). \r\n\r\nI will update my result once I have installed newer cuda and cudnn.",
"> @Zhaopudark Thank you for your comment.\r\n> \r\n> Regarding the cudnn and cuda version, I searched around and finally found a matrix [here](https://www.tensorflow.org/install/source#gpu).\r\n> \r\n> I will update my result once I have installed newer cuda and cudnn.\r\n\r\nThank you guys. \r\n\r\nAfter I have installed cuda12.3+cudnn8.9 ( I am using TF 2.16 )and set those environment paths following @Zhaopudark 's guide, GPU can be successfully detected under wsl2(Ubuntun 22.04).\r\n\r\nHope this ticket helps those who are looking for answers.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63341\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63341\">No</a>\n",
"i install cuda 12.3.2 and cudnn .then when i install tensorflow via `pip install tensorflow[and-cuda]` will auto install`tensorflow 2.16.1` and can not find my GPU. when i input `pip install tensorflow==2.15` . It will autoremove 2.16.1 and install 2.15.then I can find my GPU\r\n ```\r\npython3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n```\r\nbash output\r\n```\r\npython3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n2024-03-13 02:41:48.797545: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2024-03-13 02:41:48.797600: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2024-03-13 02:41:48.798179: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2024-03-13 02:41:48.801510: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-03-13 02:41:49.293727: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-03-13 02:41:49.828549: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:08:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-13 02:41:49.934333: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:08:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-13 02:41:49.934403: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:08:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n```",
"I am stuck with the same issue while upgrading to Tensorflow 2.16 within an Anaconda environment, with an hope to get started with Keras 3.0 released by @fchollet \r\n\r\nI am using Ubuntu on WSL2 and have never set any path variables explicitly starting from TF 2.15 on different machine\r\n\r\n**Starting from TF 2.15, this process was automated, but again we are back (at least for the moment)**\r\n\r\nTensorFlow team might've missed that setting where they set path when the virtual environment is activated, original motivation was to have different version of CUDA and cuDNN installed in different virtual environments for different TF versions.\r\n\r\nI am hopeful this will be fixed soon @tensorflower-gardener ",
"**Listing when just downgraded to TF 2.15 in same environment**\r\n<pre>\r\n(py3) chaudhary@LAPTOP:~$ python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n2024-03-14 17:54:21.028849: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2024-03-14 17:54:21.028973: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2024-03-14 17:54:21.029714: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2024-03-14 17:54:21.034104: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-03-14 17:54:21.620019: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-03-14 17:54:23.291920: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-14 17:54:23.317718: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-14 17:54:23.317830: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n</pre>\r\n\r\n\r\n**The GPU device cannot be detected however for Tensorflow 2.16**\r\n<pre>\r\n(py3) chaudhary@LAPTOP:~$ python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n2024-03-14 17:49:42.014773: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-03-14 17:49:42.753522: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-03-14 17:49:43.943419: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-14 17:49:43.943633: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\n[]\r\n</pre>\r\n",
"hi guys, this is what works for me.\r\n\r\nInstallation summary, following build configs https://www.tensorflow.org/install/source#gpu\r\nWsl distro : Ubuntu-22.04\r\nMiniconda with python 3.10\r\n\r\nCuda toolkit 12.3 [cuda](https://developer.nvidia.com/cuda-12-3-2-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local)\r\n\r\nCudnn 8.9\r\nuse older version, cudnn 8.9 [archive](https://developer.nvidia.com/rdp/cudnn-archive)\r\nfollow commands replace with 8.9 version : [cmds](https://developer.nvidia.com/cudnn-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_local)\r\n\r\nAdded these to .bashrc, and source it\r\n```\r\nexport PATH=/usr/local/cuda-12.3/bin:$PATH\r\nexport LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64:$LD_LIBRARY_PATH\r\n```\r\n\r\nvideo reference : https://www.youtube.com/watch?v=VE5OiQSfPLg\r\n\r\nresult:\r\n```\r\n2024-03-17 10:34:59.899645: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-03-17 10:35:00.567105: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-03-17 10:35:01.338148: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:07:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-17 10:35:01.393266: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:07:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-17 10:35:01.393375: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:07:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n```\r\n\r\nIf you think it too much of hassle, i would recommend to use [tensorflow/tensorflow:latest-gpu](https://hub.docker.com/layers/tensorflow/tensorflow/latest-gpu/images/sha256-4ab9ffddd6ffacc9251ac6439f431eb38d66200d3f52397b5d77f9bc3298c4e9?context=explore) . \r\nI tried both ways, realized container way train the same model faster(likely due hardware configs) and setup time is quick with no issue.",
"@chaudharyachint08. This weekend, I have shared my steps in this [post](https://little-train.com/posts/4f6afcf2.html). Maybe you can check it and transfer it to your own environment.",
"Thanks @zjm008 and @Zhaopudark for providing with workarounds. I certainly agree that these are ways to mitigate this problem\r\n<br><br>\r\n\r\nHowever, while following the [pip-installation guide](https://www.tensorflow.org/install/pip) I can see that\r\n<pre>\r\npip install tensorflow[and-cuda]\r\n</pre>\r\ndo download many cuda libraries including cuDNN as well, which was set to path variables as soon as you activate a conda environment which is a very nice functionality up to TF 2.15\r\n\r\nI believe the developers might've missed the same while moving to TF 2.16\r\n\r\nDo let me know if you two agree?",
"@chaudharyachint08 I agree with you. Actually, this should not occur, we, users should not need to install cuda and cudnn libraries twice. So, there must be some potential mistakes on optional dependency `[and-cuda]`. I have done some ablation tests before, and I find even though I use conda to install cuda and manually install cudnn to my conda env, the optional dependency `[and-cuda]` is still needed when installing TensorFlow-GPU. ",
"> @chaudharyachint08. This weekend, I have shared my steps in this [post](https://little-train.com/posts/4f6afcf2.html). Maybe you can check it and transfer it to your own environment.\r\n\r\n@Zhaopudark Wow, thank you so much for this!",
"# Almost final and automated fix below\r\n\r\n* Where I found the resolution\r\n * [TF 2.16.1 Fails to work with GPUs](https://github.com/tensorflow/tensorflow/issues/63362)\r\n * Solution proposed by \"sh-shahrokhi\", improved by \"ChristofKaufmann\"\r\n * See specially [Comment by COntributor](https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1990133275)\r\n * Related Issues\r\n * [GPU not detected on WSL2](https://github.com/tensorflow/tensorflow/issues/63341), where I have post some comments\r\n * [Tensorflow WSL GPU CUDA recognition issue RTX3090](https://github.com/tensorflow/tensorflow/issues/63948)\r\n * [Once gain: tf.2.16.1 fails to recognize GPUs](https://github.com/keras-team/keras/issues/19276)\r\n * Other mention on social media\r\n * [Reddit issue](https://www.reddit.com/r/bashonubuntuonwindows/comments/1bb0xqg/your_kernel_may_have_been_built_without_numa/)\r\n\r\n* Exact solution\r\n * Temporary fix (after activating environment in which Tensorflow 2.16.1 is installed)\r\n <pre>\r\n export NVIDIA_DIR=$(dirname $(dirname $(python -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\")))\r\n export LD_LIBRARY_PATH=$(echo ${NVIDIA_DIR}/*/lib/ | sed -r 's/\\s+/:/g')${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}\r\n </pre>\r\n * Automating the variable set/unset process with Anaconda (one-time setup)\r\n * Activate your environement in which TF 2.16.1 is installed\r\n * Two files to be created in \"anaconda3/envs/<ENV_NAME>/etc/conda\"\r\n * anaconda3/envs/<ENV_NAME>/etc/conda/activate.d/env_vars.sh\r\n <pre>\r\n #!/bin/sh\r\n export NVIDIA_DIR=$(dirname $(dirname $(python -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\")))\r\n export LD_LIBRARY_PATH=$(echo ${NVIDIA_DIR}/*/lib/ | sed -r 's/\\s+/:/g')${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}\r\n </pre>\r\n * anaconda3/envs/<ENV_NAME>/etc/conda/deactivate.d/env_vars.sh\r\n <pre>\r\n #!/bin/sh\r\n unset NVIDIA_DIR\r\n unset LD_LIBRARY_PATH\r\n </pre>\r\n * Official documentation to do this via [conda.io](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#macos-and-linux)\r\n * Stack-overflow question where I got this [Set environment vars when activating conda env](https://stackoverflow.com/questions/31598963/how-to-set-specific-environment-variables-when-activating-conda-environment)\r\n\r\n\r\n* What else helped me\r\n * [How to list all variables names and their current values?](https://askubuntu.com/questions/275965/how-to-list-all-variables-names-and-their-current-values)\r\n * [Conda documenation for \"env_vars.sh\" for activate/deactivate](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#macos-and-linux)\r\n"
] | 2024-03-09T09:42:14 | 2024-04-09T08:21:55 | 2024-03-10T08:46:38 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
WSL2 Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi,
I am trying to install run keras3 on wsl2. But the output of device listing indicates that there is still some dynamic library missing.
I followed the [doc](https://www.tensorflow.org/install/pip#step-by-step_instructions) to finish the installation
I am using the latest NV driver, version of which can be find on nvidia-smi output.
I have managed to install CUDA 11.8 and cudnn 8.6 on my device which are the exact the same version listed on doc.
Asides from installing cudnn using [Local Installer for Ubuntu22.04 x86_64 (Deb)](https://developer.nvidia.com/compute/cudnn/secure/8.6.0/local_installers/11.8/cudnn-local-repo-ubuntu2204-8.6.0.163_1.0-1_amd64.deb) on [this page](https://developer.nvidia.com/rdp/cudnn-archive), I have also tried to manually copy the cudnn header files and lib files to cuda-11.8/include and cuda-11.8/lib and the LD_LIBRARY_PATH is updated according to this [artical](https://discuss.tensorflow.org/t/tensorflow-on-wsl2-cannot-find-my-gpu-despite-following-the-guide-for-tensorflow2-12/15906) and this [one](https://dev.to/_jhohannes/installing-tensorflowwith-gpu-support-in-linux-or-wsl-2le5)
Anything I missed in my steps?
Regards
Sichao Hu
### Standalone code to reproduce the issue
```shell
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
```
### Relevant log output
```shell
TF output:
2024-03-09 16:22:45.531685: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-09 16:22:45.787261: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-09 16:22:46.518153: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-03-09 16:22:47.403086: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-09 16:22:47.474378: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[]
This is the output of nvidia-smi:
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.60.01 Driver Version: 551.76 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4070 ... On | 00000000:01:00.0 On | N/A |
| 0% 44C P8 18W / 285W | 1125MiB / 16376MiB | 5% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
This is the output of nvcc -V:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:33:58_PDT_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0
```
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PR_kwDOArmXAs5pI9C8
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PR #10388: Address lost changes for pr 7854
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PR #10388: Address lost changes for pr 7854
Imported from GitHub PR https://github.com/openxla/xla/pull/10388
Copybara import of the project:
--
d55af60f1335d82fc9c32d0dfb34708cb32174a3 by TJ <[email protected]>:
Address lost changes for pr https://github.com/openxla/xla/pull/7854
Merging this change closes #10388
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10388 from Tixxx:tixxx/coll_matmul_fix d55af60f1335d82fc9c32d0dfb34708cb32174a3
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Aborted (core dumped) in tf.linalg.solve
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[
"@vwrewsge The TF version 1.x is not actively supported. Could you please upgrade to the latest version? I tried to replicate the issue on colab using TF version 2.15 and didn't face the error reported.\r\nPlease find the attached gist [here](https://colab.research.google.com/gist/sushreebarsa/85099eae287bda0224ac04e2d32dfa48/63339.ipynb) and let us know?\r\nThank you!",
"> @vwrewsge The TF version 1.x is not actively supported. Could you please upgrade to the latest version? I tried to replicate the issue on colab using TF version 2.15 and didn't face the error reported. Please find the attached gist [here](https://colab.research.google.com/gist/sushreebarsa/85099eae287bda0224ac04e2d32dfa48/63339.ipynb) and let us know? Thank you!\r\n\r\nI'm sorry, I entered the wrong TF version. The version I tested is 2.15. Here is the feedback I just tested.\r\n```\r\n2024-03-13 10:01:35.768292: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)\r\nAborted (core dumped)\r\n```",
"Additionally, the Python version I'm using is 3.9",
"@vwrewsge Thanks for the response!\r\nCould you confirm if you have checked the colab referred to in the above which is showing the below output;\r\n`InvalidArgumentError: {{function_node __wrapped__MatrixSolve_device_/job:localhost/replica:0/task:0/device:CPU:0}} All input tensors must have the same rank. [Op:MatrixSolve]` rather than the core dumped?\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63339\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63339\">No</a>\n"
] | 2024-03-09T04:55:36 | 2024-03-31T01:48:39 | 2024-03-31T01:48:36 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 1.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.linalg.solve` encountered `Aborted (core dumped)` issue when processing random data
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
# Generate random input data
matrix = np.random.rand(2, 3)
rhs = np.random.rand(3)
# Invoke tf.linalg.solve
result = tf.linalg.solve(matrix, rhs)
# Print the result
print(result)
```
### Relevant log output
```shell
2024-03-09 04:50:01.755277: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
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PR_kwDOArmXAs5pIsv8
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Integrate LLVM at llvm/llvm-project@e93489c434da
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Integrate LLVM at llvm/llvm-project@e93489c434da
Updates LLVM usage to match
[e93489c434da](https://github.com/llvm/llvm-project/commit/e93489c434da)
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[
"Hi **@vwrewsge** ,\r\n\r\nI tried to run your code on colab using tf-nightly and faced same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/6f03e466fa02ef42e060e3e810159132/63337_tf-nightly-v.ipynb) here for reference. We need to work on it, In few days i will provide a solution for it. \r\n\r\nThank you!",
"Hi **@vwrewsge** ,\r\n- I tried alternative approaches for compression_method and window_bits. I hope it will use for you. Here i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/1706a164371da98154b50b3db6232184/63337_nightly-v.ipynb) for your reference.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"> Hi **@vwrewsge** ,\r\n> \r\n> * I tried alternative approaches for compression_method and window_bits. I hope it will use for you. Here i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/1706a164371da98154b50b3db6232184/63337_nightly-v.ipynb) for your reference.\r\n> \r\n> Thank you!\r\n\r\nThank you for your reply. Would it be better to add a validation check to handle these edge cases? Something like [Fix checkfail](https://github.com/tensorflow/tensorflow/pull/63784)"
] | 2024-03-09T03:15:46 | 2024-05-30T04:08:35 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have tested on tensorflow-nightly.
There are five bugs related to `tf.io.TFRecordWriter`.
BUG 1: `compression_method=3` -> Aborted (core dumped)
Code:
```
import tensorflow as tf
input_data = b'input_data_example'
options = tf.io.TFRecordOptions(compression_type='ZLIB', compression_method=3)
with tf.io.TFRecordWriter('output.tfrecord', options=options) as writer:
writer.write(input_data)
```
BUG 2: `window_bits=-8` -> Aborted (core dumped)
CODE:
```
import tensorflow as tf
input_data = b'input_data_example'
options = tf.io.TFRecordOptions(
compression_type='ZLIB',
window_bits=-8 # negative window_bits
)
with tf.io.TFRecordWriter('output.tfrecord', options=options) as writer:
writer.write(input_data)
```
BUG3: `compression_level=-9` -> Aborted (core dumped)
CODE:
```
import tensorflow as tf
input_data = b'input_data_example'
options = tf.io.TFRecordOptions(compression_type='ZLIB', compression_level=-9)
with tf.io.TFRecordWriter('output.tfrecord', options=options) as writer:
writer.write(input_data)
```
BUG 4: `mem_level=-10` -> Aborted (core dumped)
CODE:
```
import tensorflow as tf
input_data = b'input_data_example'
options = tf.io.TFRecordOptions(compression_type='ZLIB', mem_level=-10)
with tf.io.TFRecordWriter('output.tfrecord', options=options) as writer:
writer.write(input_data)
```
BUG 5: `compression_strategy=-2` -> Aborted (core dumped)
```
import tensorflow as tf
input_data = b'input_data_example'
options = tf.io.TFRecordOptions(compression_type='ZLIB', compression_strategy=-2)
with tf.io.TFRecordWriter('output.tfrecord', options=options) as writer:
writer.write(input_data)
```
### Standalone code to reproduce the issue
```shell
The reproducible test cases are provided above.
```
### Relevant log output
```shell
All the above codes output `Aborted (core dumped)`.
```
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PR_kwDOArmXAs5pIjb4
| 63,336 |
Update XLA visibility for Enzyme
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Update XLA visibility for Enzyme
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| 2,176,983,566 |
PR_kwDOArmXAs5pIgkn
| 63,335 |
PR #10388: Address lost changes for pr 7854
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NONE
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PR #10388: Address lost changes for pr 7854
Imported from GitHub PR https://github.com/openxla/xla/pull/10388
Copybara import of the project:
--
d55af60f1335d82fc9c32d0dfb34708cb32174a3 by TJ <[email protected]>:
Address lost changes for pr https://github.com/openxla/xla/pull/7854
Merging this change closes #10388
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10388 from Tixxx:tixxx/coll_matmul_fix d55af60f1335d82fc9c32d0dfb34708cb32174a3
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PR_kwDOArmXAs5pIgML
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[xla] An operand shared by multiple backward chains should only needed to be
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NONE
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[xla] An operand shared by multiple backward chains should only needed to be
cloned once.
This is to prepare for the next change to support the use of loop variant
parameters in a backward chain.
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PR_kwDOArmXAs5pIfFf
| 63,333 |
Add relative difference annotations to the HLO module using Statistic in google3/third_party/tensorflow/compiler/xla/xla_data.proto. Also dump DOT string as a text file. The graph renders each instruction (involving floats) colored according to the relative difference as a result of perturbation.
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Add relative difference annotations to the HLO module using Statistic in google3/third_party/tensorflow/compiler/xla/xla_data.proto. Also dump DOT string as a text file. The graph renders each instruction (involving floats) colored according to the relative difference as a result of perturbation.
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Modify hlo_graph_dumper to accept custom hash map containing HloInstruction and its corresponding color.
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Fixing IfThisThenThatLin on tf_to_mlrt pass
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Fixing IfThisThenThatLin on tf_to_mlrt pass
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Remove CreateTensorListOpsDecompositionPass when lowering TF to StableHLO, as TensorList* ops can not lower to StableHLO.
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Remove CreateTensorListOpsDecompositionPass when lowering TF to StableHLO, as TensorList* ops can not lower to StableHLO.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63088 from tensorflow:SuryanarayanaY-patch-13 6dec10d4cbce856abb2f9ebdd61a726ce0655f1d
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Added support of C++20 for OSS TF for Linux platform
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Added support of C++20 for OSS TF for Linux platform
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#tf-data Update int64_t to size_t for IndexMapperFn.
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#tf-data Update int64_t to size_t for IndexMapperFn.
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New host-event traceme added.
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New host-event traceme added.
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Validate output to operand aliasing field.
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Validate output to operand aliasing field.
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Remove tensorflow/third_party/mkl/{BUILD,build_defs.bzl} and replace with direct usage of TSL targets
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Remove tensorflow/third_party/mkl/{BUILD,build_defs.bzl} and replace with direct usage of TSL targets
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#shlo_ref Add RETURN_ON_ERROR macro to ease absl::Status handling.
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#shlo_ref Add RETURN_ON_ERROR macro to ease absl::Status handling.
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#shlo_ref Add `abs` op.
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#shlo_ref Add `abs` op.
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#shlo_ref Add helpers for unary element-wise ops.
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#shlo_ref Add helpers for unary element-wise ops.
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#shlo_ref Add `BaselineType` overload for the tensor element_type variant.
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#shlo_ref Add `BaselineType` overload for the tensor element_type variant.
This allows comparing element types between tensors without extracting them.
```cpp
if(BaselineType(tensor.element_type()) == BaselineType(tensor2.element_type())) {
// ...
}
```
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[XLA] Don't rewrite layouts for async calls
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[XLA] Don't rewrite layouts for async calls
Specifically don't rewrite the operand layouts of async computations that are executed in foreign threads. This is due to LayoutAssignment not processing those computations, but still processing their operands, which makes it possible to end up with mismatching layouts between operands and computation.
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PR_kwDOArmXAs5pIOxc
| 63,319 |
Always propagate shardings along the batch dimension in `InferConvolutionShardingFromOperands`.
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Always propagate shardings along the batch dimension in `InferConvolutionShardingFromOperands`.
Before this cl, if the kernel is large, we replicate the output, even along the batch dimension. This cl enables the sharding propagation between lhs and output along the batch dimension such that we can support data parallelism.
Add a test case, which fails before this cl and succeeds with this cl.
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PR_kwDOArmXAs5pIHSX
| 63,318 |
Rollback https://github.com/openxla/xla/pull/9757.
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Rollback https://github.com/openxla/xla/pull/9757.
It breaks internal builds.
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PR_kwDOArmXAs5pIFog
| 63,317 |
Change access to elsize attribute for NumPy 2.0 compatibility
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Change access to elsize attribute for NumPy 2.0 compatibility
See https://numpy.org/devdocs/numpy_2_0_migration_guide.html#the-pyarray-descr-struct-has-been-changed
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PR_kwDOArmXAs5pIBfk
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Integrate LLVM at llvm/llvm-project@9baa4140f2b6
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Integrate LLVM at llvm/llvm-project@9baa4140f2b6
Updates LLVM usage to match
[9baa4140f2b6](https://github.com/llvm/llvm-project/commit/9baa4140f2b6)
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PR_kwDOArmXAs5pH69O
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PR #10082: [ROCm] Enable GpuCommandBuffer::Trace
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PR #10082: [ROCm] Enable GpuCommandBuffer::Trace
Imported from GitHub PR https://github.com/openxla/xla/pull/10082
Copybara import of the project:
--
e7c8de8fcdadd7bf9b02e4e79afe941afc35d190 by Dragan Mladjenovic <[email protected]>:
[ROCm] Enable GpuCommandBuffer::Trace
--
b66e16f53a9a6d6a44f51439a5b39e2670effaf7 by Dragan Mladjenovic <[email protected]>:
[ROCm] Allow capturing operations to command buffer that require tracing
Merging this change closes #10082
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10082 from ROCm:cmmand_trace b66e16f53a9a6d6a44f51439a5b39e2670effaf7
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PR_kwDOArmXAs5pH2or
| 63,314 |
#shlo_ref Add utilities library.
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#shlo_ref Add utilities library.
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I_kwDOArmXAs6Bvkbn
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Tensorflow 2.15.1 wheels are not available on pypi
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NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.15.1
### Custom code
No
### OS platform and distribution
Any
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Tensorflow 2.15.1 wheels (also 2.16.1) are not uploaded to pypi.
### Standalone code to reproduce the issue
```shell
$ pip install tensorflow==2.15.1
```
### Relevant log output
```shell
ERROR: Could not find a version that satisfies the requirement tensorflow==2.15.1 (from versions: 2.16.0rc0)
ERROR: No matching distribution found for tensorflow==2.15.1
```
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PR_kwDOArmXAs5pHnBo
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[XLA:Python] Port pmap to nanobind.
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[XLA:Python] Port pmap to nanobind.
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PR_kwDOArmXAs5pHcOv
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Support `stablehlo.convolution` legalization to `tfl.transpose_conv` op.
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Support `stablehlo.convolution` legalization to `tfl.transpose_conv` op.
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[XLA:Python] Port pjit to nanobind.
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[XLA:Python] Port pjit to nanobind.
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Fix method name.
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Fix method name.
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Remove unused portions of stream_executor::DeviceOptions.
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Remove unused portions of stream_executor::DeviceOptions.
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PR_kwDOArmXAs5pHBEK
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Add PYTORCH to ModelType enum in conversion_metadata schema
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Add PYTORCH to ModelType enum in conversion_metadata schema
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Use the `original token name` and not the node name since names can change for many reasons.
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Use the `original token name` and not the node name since names can change for many reasons.
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TaskQueue should remove all tasks when requested to remove tasks up to a size bigger than the total task size in the queue
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TaskQueue should remove all tasks when requested to remove tasks up to a size bigger than the total task size in the queue
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PR_kwDOArmXAs5pGzTN
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Add new dimension error_message to file logger metrics.
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Add new dimension error_message to file logger metrics.
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PR_kwDOArmXAs5pGwCK
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Fix ClangTidy warnings.
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Fix ClangTidy warnings.
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Unable to build the pip package after building the package builder
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"Please see https://github.com/tensorflow/tensorflow/issues/63298, particularly https://github.com/tensorflow/tensorflow/issues/63298#issuecomment-1986141742",
"@vladbelit , i ran into that issue mentioned in #63298 previously. In order to fix it i editted the \"/home/administrator/tensorinstall/tensorflow/tensorflow/tools/pip_package/BUILD\" file. \r\ni changed the following lines after which the package built succesfully\r\n\r\npy_binary(\r\n name = \"build_pip_package\", # changed from \"build_pip_package_py\"\r\n srcs = [\"build_pip_package.py\"],\r\n main = \"build_pip_package.py\",",
"> @vladbelit , i ran into that issue mentioned in #63298 previously. In order to fix it i editted the \"/home/administrator/tensorinstall/tensorflow/tensorflow/tools/pip_package/BUILD\" file. i changed the following lines after which the package built succesfully\r\n> \r\n> py_binary( name = \"build_pip_package\", # changed from \"build_pip_package_py\" srcs = [\"build_pip_package.py\"], main = \"build_pip_package.py\",\r\n\r\nSAME error, thanks, package has built succesfully"
] | 2024-03-08T17:24:19 | 2024-03-14T09:03:02 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
6.1.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
8.9
### GPU model and memory
NVIDIA A100-SXM4-80GB
### Current behavior?
After running the bazel package builder i am trying to build the pip package when i see the following error. Also important to note that i had to supply the --output-name and --project-name flags (since they are mandatory). This is not reflected in the guide to build tf from source. I also dont understand what headers need to be supplied. I see a command line option to supply headers though for the whl build.
Traceback (most recent call last):
File "/home/administrator/tensorinstall/tensorflow/./bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow/tools/pip_package/build_pip_package.py", line 333, in <module>
prepare_wheel_srcs(args.headers, args.srcs, args.xla_aot,
File "/home/administrator/tensorinstall/tensorflow/./bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow/tools/pip_package/build_pip_package.py", line 175, in prepare_wheel_srcs
prepare_headers(headers, os.path.join(srcs_dir, "tensorflow/include"))
File "/home/administrator/tensorinstall/tensorflow/./bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow/tools/pip_package/build_pip_package.py", line 90, in prepare_headers
for file in headers:
TypeError: 'NoneType' object is not iterable
### Standalone code to reproduce the issue
```shell
bazel build //tensorflow/tools/pip_package:build_pip_package
./bazel-bin/tensorflow/tools/pip_package/build_pip_package --output-name /tmp/tensorflow_pkg --project-name tensor
```
### Relevant log output
```shell
sudo ./bazel-bin/tensorflow/tools/pip_package/build_pip_package --output-name /tmp/tensorflow_pkg --project-name tensor
Traceback (most recent call last):
File "/home/administrator/tensorinstall/tensorflow/./bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow/tools/pip_package/build_pip_package.py", line 333, in <module>
prepare_wheel_srcs(args.headers, args.srcs, args.xla_aot,
File "/home/administrator/tensorinstall/tensorflow/./bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow/tools/pip_package/build_pip_package.py", line 175, in prepare_wheel_srcs
prepare_headers(headers, os.path.join(srcs_dir, "tensorflow/include"))
File "/home/administrator/tensorinstall/tensorflow/./bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow/tools/pip_package/build_pip_package.py", line 90, in prepare_headers
for file in headers:
TypeError: 'NoneType' object is not iterable
```
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[xla:cpu] Remove support for sparse shapes
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[xla:cpu] Remove support for sparse shapes
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[XLA:Python] Port jax_jit module to nanobind.
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[XLA:Python] Port jax_jit module to nanobind.
Remove some deprecated code in pmap_lib to check for jax.config._trace_context, which no longer exists at head.
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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/ca0f1e02f74aa831a884b8989efecec05f0e9aed.
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Target 'build_pip_package' no longer existing?
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[
"This has been removed with recent refactoring. I think @georgiyekkert can help here with the new way to build",
"The new target (at HEAD) is ` //tensorflow/tools/pip_package:wheel`. It's a somewhat more efficient way of building the wheel \r\nAt 2.16, it's `//tensorflow/tools/pip_package/v2:wheel`\r\n\r\n\r\nThe build command might now look something like\r\n```\r\nbazel build --repo_env=TF_PYTHON_VERSION=3.11 --config release_cpu_linux //tensorflow/tools/pip_package/v2:wheel --repo_env=WHEEL_NAME=tf_nightly\r\n```\r\nSee https://github.com/tensorflow/tensorflow/commit/d52ee229e5e87233e6c5352db8ff4b271cbc0d4c for more relevant details\r\n\r\n",
"@vladbelit , i cant seem to find v2 inside pip_package while building. I tried after pulling from master and r2.15\r\n\r\nERROR: no such package 'tensorflow/tools/pip_package/v2': BUILD file not found in any of the following directories. Add a BUILD file to a directory to mark it as a package.\r\n - /home/administrator/tensorinstall/tensorflow/tensorflow/tensorflow/tools/pip_package/v2",
"Indeed, same error as sumanth1989:\r\n\r\n`ERROR: no such package 'tensorflow/tools/pip_package/v2': BUILD file not found in any of the following directories. `\r\n",
"@waarmond i could clear this error but it fails while building the pip package. If you want to clear this error do the following :\r\n\r\n\"/home/administrator/tensorinstall/tensorflow/tensorflow/tools/pip_package/BUILD\" file.\r\ni changed the following lines after which the package built succesfully\r\n# changed the name from \"build_pip_package_py\" to \"build_pip_package\"\r\npy_binary(\r\nname = \"build_pip_package\", \r\nsrcs = [\"build_pip_package.py\"],\r\nmain = \"build_pip_package.py\",",
"If you are building the nightly CPU wheel from master HEAD, please run:\r\n\r\n`bazel build --repo_env=TF_PYTHON_VERSION=3.11 //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tf_nightly`\r\n\r\nI have updated instructions but it takes time for them to propagate. I will post here once https://www.tensorflow.org/install/source is updated. ",
"@georgiyekkert it still doesnt work for me, i have tried with bazel 6.1.0 ,6.5.0 ,7.0.2. I get different errors depending on the bazel version. Btw i am trying to build for GPU\r\n\r\nCan you tell me which version you are using ?\r\nIm doing bazel 7.0.2 , cuda 12.2 , cudnn 8.9, python3.10",
"@sumanth1989 You should always use the version from https://github.com/tensorflow/tensorflow/blob/master/.bazelversion",
"Using the correct bazel version mentioned in .bazelversion (6.5.0) it still didnt work\r\n\r\nError while using clang 17 below \r\n```\r\nINFO: Analyzed target //tensorflow/tools/pip_package:wheel (718 packages loaded, 51809 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /home/administrator/.cache/bazel/_bazel_administrator/38c227b95c25a75c2f31c75458f8f9be/external/com_google_absl/absl/log/internal/BUILD.bazel:227:11: Compiling absl/log/internal/nullguard.cc failed: (Exit 1): clang failed: error executing command (from target @com_google_absl//absl/log/internal:nullguard) /usr/lib/llvm-17/bin/clang -MD -MF bazel-out/k8-opt/bin/external/com_google_absl/absl/log/internal/_objs/nullguard/nullguard.pic.d ... (remaining 78 arguments skipped)\r\nclang: warning: argument unused during compilation: '--cuda-path=/usr/local/cuda-12.2' [-Wunused-command-line-argument]\r\nIn file included from external/com_google_absl/absl/log/internal/nullguard.cc:15:\r\nexternal/com_google_absl/absl/log/internal/nullguard.h:27:10: fatal error: 'array' file not found\r\n 27 | #include <array>\r\n | ^~~~~~~\r\n1 error generated.\r\nERROR: /home/administrator/.cache/bazel/_bazel_administrator/38c227b95c25a75c2f31c75458f8f9be/external/llvm-project/llvm/BUILD.bazel:191:11: Compiling llvm/lib/Demangle/MicrosoftDemangleNodes.cpp failed: (Exit 1): clang failed: error executing command (from target @llvm-project//llvm:Demangle) /usr/lib/llvm-17/bin/clang -MD -MF bazel-out/k8-opt/bin/external/llvm-project/llvm/_objs/Demangle/MicrosoftDemangleNodes.pic.d ... (remaining 86 arguments skipped)\r\nclang: warning: argument unused during compilation: '--cuda-path=/usr/local/cuda-12.2' [-Wunused-command-line-argument]\r\nIn file included from external/llvm-project/llvm/lib/Demangle/MicrosoftDemangleNodes.cpp:13:\r\nexternal/llvm-project/llvm/include/llvm/Demangle/MicrosoftDemangleNodes.h:16:10: fatal error: 'array' file not found\r\n 16 | #include <array>\r\n | ^~~~~~~\r\n1 error generated.\r\nERROR: /home/administrator/.cache/bazel/_bazel_administrator/38c227b95c25a75c2f31c75458f8f9be/external/llvm-project/llvm/BUILD.bazel:191:11: Compiling llvm/lib/Demangle/RustDemangle.cpp [for tool] failed: (Exit 1): clang failed: error executing command (from target @llvm-project//llvm:Demangle) /usr/lib/llvm-17/bin/clang -MD -MF bazel-out/k8-opt-exec-50AE0418/bin/external/llvm-project/llvm/_objs/Demangle/RustDemangle.d ... (remaining 76 arguments skipped)\r\nIn file included from external/llvm-project/llvm/lib/Demangle/RustDemangle.cpp:14:\r\nexternal/llvm-project/llvm/include/llvm/Demangle/Demangle.h:12:10: fatal error: 'cstddef' file not found\r\n 12 | #include <cstddef>\r\n | ^~~~~~~~~\r\n1 error generated.\r\nERROR: /home/administrator/.cache/bazel/_bazel_administrator/38c227b95c25a75c2f31c75458f8f9be/external/highwayhash/BUILD.bazel:40:11: Compiling highwayhash/arch_specific.cc failed: (Exit 1): clang failed: error executing command (from target @highwayhash//:arch_specific) /usr/lib/llvm-17/bin/clang -MD -MF bazel-out/k8-opt/bin/external/highwayhash/_objs/arch_specific/arch_specific.pic.d ... (remaining 43 arguments skipped)\r\nclang: warning: argument unused during compilation: '--cuda-path=/usr/local/cuda-12.2' [-Wunused-command-line-argument]\r\nexternal/highwayhash/highwayhash/arch_specific.cc:35:10: fatal error: 'string' file not found\r\n 35 | #include <string>\r\n | ^~~~~~~~\r\n1 error generated.\r\nERROR: /home/administrator/.cache/bazel/_bazel_administrator/38c227b95c25a75c2f31c75458f8f9be/external/com_google_absl/absl/base/BUILD.bazel:77:11: Compiling absl/base/internal/raw_logging.cc failed: (Exit 1): clang failed: error executing command (from target @com_google_absl//absl/base:raw_logging_internal) /usr/lib/llvm-17/bin/clang -MD -MF bazel-out/k8-opt/bin/external/com_google_absl/absl/base/_objs/raw_logging_internal/raw_logging.pic.d ... (remaining 78 arguments skipped)\r\nclang: warning: argument unused during compilation: '--cuda-path=/usr/local/cuda-12.2' [-Wunused-command-line-argument]\r\nIn file included from external/com_google_absl/absl/base/internal/raw_logging.cc:15:\r\nexternal/com_google_absl/absl/base/internal/raw_logging.h:22:10: fatal error: 'string' file not found\r\n 22 | #include <string>\r\n | ^~~~~~~~\r\n1 error generated.\r\nERROR: /home/administrator/tensorinstall/tensorflow/tensorflow/lite/kernels/internal/BUILD:545:11: Compiling tensorflow/lite/kernels/internal/quantization_util.cc failed: (Exit 1): clang failed: error executing command (from target //tensorflow/lite/kernels/internal:quantization_util) /usr/lib/llvm-17/bin/clang -MD -MF bazel-out/k8-opt/bin/tensorflow/lite/kernels/internal/_objs/quantization_util/quantization_util.pic.d ... (remaining 48 arguments skipped)\r\nclang: warning: argument unused during compilation: '--cuda-path=/usr/local/cuda-12.2' [-Wunused-command-line-argument]\r\nIn file included from tensorflow/lite/kernels/internal/quantization_util.cc:16:\r\n./tensorflow/lite/kernels/internal/quantization_util.h:18:10: fatal error: 'cmath' file not found\r\n 18 | #include <cmath>\r\n | ^~~~~~~\r\n1 error generated.\r\nTarget //tensorflow/tools/pip_package:wheel failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 16.935s, Critical Path: 1.31s\r\nINFO: 393 processes: 354 internal, 39 local.\r\nFAILED: Build did NOT complete successfully\r\n\r\nError while using GCC\r\n\r\nERROR: /home/administrator/tensorinstall/tensorflow/tensorflow/compiler/mlir/quantization/stablehlo/BUILD:46:11: Compiling tensorflow/compiler/mlir/quantization/stablehlo/passes/nchw_convolution_to_nhwc.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target //tensorflow/compiler/mlir/quantization/stablehlo:passes) external/local_config_cuda/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc -MD -MF ... (remaining 633 arguments skipped)\r\ntensorflow/compiler/mlir/quantization/stablehlo/passes/nchw_convolution_to_nhwc.cc: In member function ‘llvm::SmallVector<long int> mlir::quant::stablehlo::{anonymous}::RewriteNchwConvolutionToNhwc::PermuteShape(llvm::ArrayRef<long int>, llvm::ArrayRef<long int>) const’:\r\ntensorflow/compiler/mlir/quantization/stablehlo/passes/nchw_convolution_to_nhwc.cc:199: error: could not convert ‘after_shape’ from ‘SmallVector<[...],4>’ to ‘SmallVector<[...],6>’\r\n 199 | return after_shape;\r\n | \r\nTarget //tensorflow/tools/pip_package:wheel failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nERROR: /home/administrator/tensorinstall/tensorflow/tensorflow/tools/pip_package/BUILD:265:9 Action tensorflow/tools/pip_package/wheel_house failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target //tensorflow/compiler/mlir/quantization/stablehlo:passes) external/local_config_cuda/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc -MD -MF ... (remaining 633 arguments skipped)\r\nINFO: Elapsed time: 630.573s, Critical Path: 362.42s\r\nINFO: 34839 processes: 9006 internal, 25833 local.\r\nFAILED: Build did NOT complete successfully\r\n```",
"The new installation guide of @georgiyekkert worked for me ",
"@waarmond Kindly double-check the target name you're using. The error message suggests \"build_pip_package.py\" might exist. Could you ensure you're using the correct target name with a colon (\":\") separating the package and the target: `//tensorflow/tools/pip_package:build_pip_package.`\r\nPlease follow the official TensorFlow installation guide for building from source: https://www.tensorflow.org/install/source.\r\nThank you!",
"> @waarmond Kindly double-check the target name you're using. The error message suggests \"build_pip_package.py\" might exist. Could you ensure you're using the correct target name with a colon (\":\") separating the package and the target: `//tensorflow/tools/pip_package:build_pip_package.` Please follow the official TensorFlow installation guide for building from source: https://www.tensorflow.org/install/source. Thank you!\r\n\r\nThe `build_pip_package` target no longer exists. Following the newly updated doc is a good idea, however - https://www.tensorflow.org/install/source",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"The new build from source guide seems to be correct, but I can't finish a successful build with onednn 3.x: https://github.com/tensorflow/tensorflow/issues/64518",
"@waarmond Thank you for your response!\r\nCould you please confirm if we can close this issue as the build from the source guide looks good to you. So that we will track the new ticket?\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63298\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63298\">No</a>\n"
] | 2024-03-08T15:23:32 | 2024-04-11T01:48:19 | 2024-04-11T01:48:11 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
git > 2.16.1
### Custom code
No
### OS platform and distribution
ArchLinux
### Mobile device
_No response_
### Python version
3.10
### Bazel version
7.0.2 (also tried 5.3.2)
### GCC/compiler version
13.2.1
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`build_pip_package ERROR: no such target '//tensorflow/tools/pip_package:build_pip_package': target 'build_pip_package' not declared in package 'tensorflow/tools/pip_package' defined by ./tensorflow/src/tensorflow/tensorflow/tools/pip_package/BUILD (did you mean 'build_pip_package.py'? Tip: use `query "//tensorflow/tools/pip_package:*"` to see all the targets in that package)`
### Standalone code to reproduce the issue
I've tried to compile current git of TensorFlow (near to 2.16.1) with the same PKGBUILD from ArchLinux which is still at 2.15.0 :
https://gitlab.archlinux.org/archlinux/packaging/packages/tensorflow/-/blob/main/PKGBUILD?ref_type=heads
```shell
[..]
./configure
bazel \
build ${BAZEL_ARGS[@]} \
//tensorflow:libtensorflow.so \
//tensorflow:libtensorflow_cc.so \
//tensorflow:install_headers \
//tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package "${srcdir}"/tmp
```
### Relevant log output
_No response_
|
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PR #10252: [STACKED PR] [XLA:GPU][NFC] Rename Triton autotuner to GEMM fusion autotuner.
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PR #10252: [STACKED PR] [XLA:GPU][NFC] Rename Triton autotuner to GEMM fusion autotuner.
Imported from GitHub PR https://github.com/openxla/xla/pull/10252
// Testing stacked PRs.
As discussed during the addition of the cuDNN GEMM fusion backend the autotuner pass can have a better name now.
Copybara import of the project:
--
e6bf9c4a571ba1afdd3f333fc0d0e51e1c8abec9 by Ilia Sergachev <[email protected]>:
[XLA:GPU][NFC] Rename Triton GEMM autotuner to GEMM fusion autotuner.
Merging this change closes #10252
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10252 from openxla:rename_gemm_fusion_related e6bf9c4a571ba1afdd3f333fc0d0e51e1c8abec9
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PR #10252: [STACKED PR] [XLA:GPU][NFC] Rename Triton autotuner to GEMM fusion autotuner.
Imported from GitHub PR https://github.com/openxla/xla/pull/10252
// Testing stacked PRs.
As discussed during the addition of the cuDNN GEMM fusion backend the autotuner pass can have a better name now.
Copybara import of the project:
--
00341e6bc2edf6bb8be1eb5cdf6a59f6bbb39efc by Ilia Sergachev <[email protected]>:
[XLA:GPU] Fix xla_gpu_dump_autotuned_triton_fusions flag interaction with fallback to cuBLAS.
Currently using the flag leads to crashes on checks in TritonGemmConfig::FromProto() when any GEMM fusion falls back to cuBLAS.
--
4b99f304ccd1bd81bd2b99727f62dd6d50754661 by Ilia Sergachev <[email protected]>:
[XLA:GPU][NFC] Rename Triton GEMM autotuner to GEMM fusion autotuner.
Merging this change closes #10252
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10252 from openxla:rename_gemm_fusion_related 4b99f304ccd1bd81bd2b99727f62dd6d50754661
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PR_kwDOArmXAs5pFnRW
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Fix checkfail in ThreadUnsafeUnigramCandidateSampler
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The API `raw.ops.ThreadUnsafeUnigramCandidateSampler` checkfails if provided larger input to `range` argument.
Fixes #60200 .
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Add sparse dot op support in dot_dimension_sorter pass
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Add sparse dot op support in dot_dimension_sorter pass
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Help needed to load tflite model
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[
"Hi @acode-x,\r\n\r\nI have reproduced the code on Debian GNU/Linux 12 (bookworm) with TF 2.14.1. The LSTM model is successfully converted to TFLite and inferencing on the same platform also done and no errors found. Screenshots are attached below. Regarding the error which you encountered ....please confirm, on which platform you have taken the inference?\r\n\r\n\r\n\r\n\r\n\r\nThank You\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2024-03-08T13:52:44 | 2024-03-29T01:46:57 | 2024-03-29T01:46:56 |
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### 1. System information
- OS Platform and Distribution: Debian GNU/Linux 12 (bookworm)
- TensorFlow installation: pip
- TensorFlow library: 2.14.1
- tflite-runtime library: 2.14.0
### 2. Code
```python
model = Sequential([
LSTM(32, return_sequences=True, input_shape=(window_size, 1)),
Dropout(0.2),
LSTM(32, return_sequences=True),
Dropout(0.2),
LSTM(16, return_sequences=False),
Dense(n_steps_out),
])
model.save('testttt-2')
from tensorflow import lite
converter = lite.TFLiteConverter.from_saved_model('testttt-2')
converter.optimizations = [lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [lite.OpsSet.TFLITE_BUILTINS, lite.OpsSet.SELECT_TF_OPS]
tflite_model = converter.convert()
with open('testttt-2.tflite', 'wb') as f:
f.write(tflite_model)
```
#### Option B: Paste your code here or provide a link to a custom end-to-end colab
Output when converting:
```bash
2024-03-08 18:52:13.471941: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 189169 microseconds.
2024-03-08 18:52:13.616872: W tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2178] TFLite interpreter needs to link Flex delegate in order to run the model since it contains the following Select TFop(s):
Flex ops: FlexTensorListReserve, FlexTensorListSetItem, FlexTensorListStack
Details:
tf.TensorListReserve(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<?x16xf32>>>) : {device = ""}
tf.TensorListReserve(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<?x32xf32>>>) : {device = ""}
tf.TensorListSetItem(tensor<!tf_type.variant<tensor<?x16xf32>>>, tensor<i32>, tensor<?x16xf32>) -> (tensor<!tf_type.variant<tensor<?x16xf32>>>) : {device = "", resize_if_index_out_of_bounds = false}
tf.TensorListSetItem(tensor<!tf_type.variant<tensor<?x32xf32>>>, tensor<i32>, tensor<?x32xf32>) -> (tensor<!tf_type.variant<tensor<?x32xf32>>>) : {device = "", resize_if_index_out_of_bounds = false}
tf.TensorListStack(tensor<!tf_type.variant<tensor<?x16xf32>>>, tensor<2xi32>) -> (tensor<1x?x16xf32>) : {device = "", num_elements = 1 : i64}
tf.TensorListStack(tensor<!tf_type.variant<tensor<?x32xf32>>>, tensor<2xi32>) -> (tensor<?x?x32xf32>) : {device = "", num_elements = -1 : i64}
See instructions: https://www.tensorflow.org/lite/guide/ops_select
```
### 3. Failure after conversion
Output when loading and trying to infer using tflite
```bash
RuntimeError: Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter.
Make sure you apply/link the Flex delegate before inference.
For the Android, it can be resolved by adding "org.tensorflow:tensorflow-lite-select-tf-ops" dependency. See instructions: https://www.tensorflow.org/lite/guide/ops_selectNode number 5 (FlexTensorListReserve) failed to prepare.
```
I am not sure how to get this working on my edge device - Debian GNU/Linux 12 which has tflite-runtime.
Requesting guidance on same.
Thanks
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Add sparse dot op support in dot_dimension_merger pass
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Add sparse dot op support in dot_dimension_merger pass
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