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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.

Keep up-to-date with release announcements and security updates by subscribing to [email protected]. See all the mailing lists.

Install

See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.

To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):

$ pip install tensorflow

A smaller CPU-only package is also available:

$ pip install tensorflow-cpu

To update TensorFlow to the latest version, add --upgrade flag to the above commands.

Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu packages on PyPi.

Try your first TensorFlow program

$ python
>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'

For more examples, see the TensorFlow tutorials.

Contribution guidelines

If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs, please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.

The TensorFlow project strives to abide by generally accepted best practices in open-source software development:

Fuzzing Status CII Best Practices Contributor Covenant

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Comments
  • Error : Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.

    Error : Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.

    Please make sure that this is a build/installation issue. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:build_template

    System information

    • OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 16.04
    • Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
    • TensorFlow installed from (source or binary): Source and Binary (tried both)
    • TensorFlow version: 1.12
    • Python version: 3.6
    • Installed using virtualenv? pip? conda?: conda
    • Bazel version (if compiling from source): 0.18
    • GCC/Compiler version (if compiling from source): gcc 5.4.0
    • CUDA/cuDNN version: Cudnn - 7.4 , CUDA- 9.0
    • GPU model and memory: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.8225 8GB

    Describe the problem I tried installting tensorflow 1.12 using both pip install and building from source.However when I am trying to run faster rcnn model i get following error message: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.

    I only get this with tf 1.12 and python 3.6 ,it works fine with python 3.6

    Provide the exact sequence of commands / steps that you executed before running into the problem

    Any other info / logs Traceback (most recent call last): File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1334, in _do_call return fn(*args) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1319, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 2, 2], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, FeatureExtractor/MobilenetV1/Conv2d_0/weights/read/_4__cf__7)]] [[{{node Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ClipToWindow_21/Gather/GatherV2_2/_211}} = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_7500_...GatherV2_2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last): File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap self.run() File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/process.py", line 93, in run self._target(*self._args, **self._kwargs) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/pool.py", line 103, in worker initializer(*initargs) File "detection_app.py", line 67, in worker output_q.put(y.get_stats_and_detection(frame)) File "/home/user/faster_rcnn_inception_v2_coco_2018_01_28/base_model.py", line 142, in get_stats_and_detection boxes, scores, classes, num = self.processFrame(img) File "/home/user/faster_rcnn_inception_v2_coco_2018_01_28/base_model.py", line 76, in processFrame feed_dict={self.image_tensor: image_np_expanded}) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 929, in run run_metadata_ptr) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1152, in _run feed_dict_tensor, options, run_metadata) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1328, in _do_run run_metadata) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1348, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[node FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D (defined at /home/user/faster_rcnn_inception_v2_coco_2018_01_28/base_model.py:36) = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 2, 2], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, FeatureExtractor/MobilenetV1/Conv2d_0/weights/read/_4__cf__7)]] [[{{node Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ClipToWindow_21/Gather/GatherV2_2/_211}} = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_7500_...GatherV2_2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

    Caused by op 'FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D', defined at: File "detection_app.py", line 94, in pool = Pool(args.num_workers, worker, (input_q, output_q)) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/context.py", line 119, in Pool context=self.get_context()) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/pool.py", line 174, in init self._repopulate_pool() File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/pool.py", line 239, in _repopulate_pool w.start() File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/process.py", line 105, in start self._popen = self._Popen(self) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/context.py", line 277, in _Popen return Popen(process_obj) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/popen_fork.py", line 19, in init self._launch(process_obj) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/popen_fork.py", line 73, in _launch code = process_obj._bootstrap() File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap self.run() File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/process.py", line 93, in run self._target(*self._args, **self._kwargs) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/multiprocessing/pool.py", line 103, in worker initializer(*initargs) File "detection_app.py", line 62, in worker y = DetectorAPI() File "/home/user/faster_rcnn_inception_v2_coco_2018_01_28/base_model.py", line 36, in init tf.import_graph_def(od_graph_def, name='') File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 488, in new_func return func(*args, **kwargs) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/framework/importer.py", line 442, in import_graph_def _ProcessNewOps(graph) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/framework/importer.py", line 234, in _ProcessNewOps for new_op in graph._add_new_tf_operations(compute_devices=False): # pylint: disable=protected-access File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3440, in _add_new_tf_operations for c_op in c_api_util.new_tf_operations(self) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3440, in for c_op in c_api_util.new_tf_operations(self) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3299, in _create_op_from_tf_operation ret = Operation(c_op, self) File "/home/user/anaconda3/envs/tf_faust/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1770, in init self._traceback = tf_stack.extract_stack()

    UnknownError (see above for traceback): Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[node FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D (defined at /home/user/faster_rcnn_inception_v2_coco_2018_01_28/base_model.py:36) = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 2, 2], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, FeatureExtractor/MobilenetV1/Conv2d_0/weights/read/_4__cf__7)]] [[{{node Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ClipToWindow_21/Gather/GatherV2_2/_211}} = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_7500_...GatherV2_2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

  • Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

    Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

    Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template

    System information

    • Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes and No (described below)
    • OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Manjaro
    • Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
    • TensorFlow installed from (source or binary): tf-nightly-gpu (Dec 19, r1.13)
    • TensorFlow version (use command below): 1.13.0-dev20181219
    • Python version: 3.7.1
    • Bazel version (if compiling from source):
    • GCC/Compiler version (if compiling from source):
    • CUDA/cuDNN version: CUDA 10 with cuDNN 7.4.1
    • GPU model and memory: RTX 2070 8GB

    Describe the current behavior I'm running the CNN model on MNIST. When I'm running with the GPU, I am encountering 2018-12-20 20:09:13.644176: E tensorflow/stream_executor/cuda/cuda_dnn.cc:334] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

    I did some digging and realized that it is a memory issue (which shouldn't be the case as I have 32GB of RAM and 64GB of swap. I ran htop when running the model and I have 20+GB free, which is more than enough to fit the 8GB vRAM mappings.

    Using the gpu_options.allow_growth = True gets the model to work properly, and setting os.environ['CUDA_VISIBLE_DEVICES'] = '-1' also works. This means that I AM facing a memory issue, but I don't see how.

    Also, using gpu_options.allow_growth = True does not fix the same issue when trying to run tensorflow/models/official/mnist/ model, which should have a similar behavior with my code.

    Code to reproduce the issue

    import os
    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    import math
    import time
    # Killing optional CPU driver warnings
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    # os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
    tf.logging.set_verbosity(tf.logging.ERROR)
    
    
    class Model:
    
        def __init__(self, image, label):
            """
            A Model class contains a computational graph that classifies images
            to predictions. Each of its methods builds part of the graph
            on Model initialization. Do not modify the constructor, as doing so
            would break the autograder. You may, however, add class variables
            to use in your graph-building. e.g. learning rate, 
    
            image: the input image to the computational graph as a tensor
            label: the correct label of an image as a tensor
            prediction: the output prediction of the computational graph,
                        produced by self.forward_pass()
            optimize: the model's optimizing tensor produced by self.optimizer()
            loss: the model's loss produced by computing self.loss_function()
            accuracy: the model's prediction accuracy
            """
            self.image = image
            self.label = label
    
            # TO-DO: Add any class variables you want to use.
    
            self.prediction = self.forward_pass()
            self.loss = self.loss_function()
            self.optimize = self.optimizer()
            self.accuracy = self.accuracy_function()
    
        def forward_pass(self):
            """
            Predicts a label given an image using convolution layers
    
            :return: the prediction as a tensor
            """
            filter_1 = tf.Variable(tf.truncated_normal([3, 3, 1, 8], stddev=0.1))
            conv_1 = tf.nn.conv2d(self.image, filter_1, [1, 1, 1, 1], "SAME")
    
            reshaped = tf.reshape(conv_1, shape=[50, -1])
    
            L1 = reshaped.shape[1].value
            L2 = 500
            W1 = tf.Variable(tf.random_normal([L1, L2], mean=0, stddev=0.01))
            b1 = tf.Variable(tf.random_normal([L2], mean=0, stddev=0.01))
            relu_1 = tf.nn.relu(tf.matmul(reshaped, W1) + b1)
    
            W2 = tf.Variable(tf.random_normal([L2, 10], mean=0, stddev=0.01))
            b2 = tf.Variable(tf.random_normal([10], mean=0, stddev=0.01))
            logits = tf.nn.relu(tf.matmul(relu_1, W2) + b2)
            return logits
    
        def loss_function(self):
            """
            Calculates the model cross-entropy loss
    
            :return: the loss of the model as a tensor
            """
            loss = tf.losses.softmax_cross_entropy(onehot_labels=self.label, logits=self.prediction)
            return loss
    
        def optimizer(self):
            """
            Optimizes the model loss using an Adam Optimizer
    
            :return: the optimizer as a tensor
            """
            learning_rate = 0.1
            sgd = tf.train.GradientDescentOptimizer(learning_rate)
            train = sgd.minimize(self.loss)
            return train
    
        def accuracy_function(self):
            """
            Calculates the model's prediction accuracy by comparing
            predictions to correct labels – no need to modify this
    
            :return: the accuracy of the model as a tensor
            """
            correct_prediction = tf.equal(tf.argmax(self.prediction, 1),
                                          tf.argmax(self.label, 1))
            return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    
    
    def main():
        t_start = time.time()
    
        mnist = input_data.read_data_sets("data/mnist/", one_hot=True)
        batch_sz = 50
        batch = 2000
    
        inputs = tf.placeholder(shape=[batch_sz, 28, 28, 1], dtype=tf.float32)
        labels = tf.placeholder(shape=[batch_sz, 10], dtype=tf.float32)
    
        model = Model(inputs, labels)
    
        session_config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
        sess = tf.Session(config=session_config)
    
        # sess = tf.Session()
    
        sess.run(tf.global_variables_initializer())
        for i in range(batch):
            next_image, next_label = mnist.train.next_batch(batch_sz)
            next_image = next_image.reshape((batch_sz, 28, 28, 1))
            sess.run(model.optimize, feed_dict={inputs: next_image, labels: next_label})
    
        acc, test_images, test_labels = 0, mnist.test.images, mnist.test.labels
        test_batch = math.ceil(len(test_images) / batch_sz)
        for i in range(test_batch):
            batch_images = test_images[i * batch_sz: (i + 1) * batch_sz]
            batch_images = batch_images.reshape((batch_sz, 28, 28, 1))
            batch_labes = test_labels[i * batch_sz: (i + 1) * batch_sz]
            acc += sess.run(model.accuracy, feed_dict={inputs: batch_images, labels: batch_labes})
        acc /= test_batch
        print(acc)
    
        print(time.time() - t_start, 'seconds')
    
        return
    
    
    if __name__ == '__main__':
        main()
    
  • Win10: ImportError: DLL load failed: The specified module could not be found

    Win10: ImportError: DLL load failed: The specified module could not be found

    System information:

    Have I written custom code: No OS Platform and Distribution: Windows 10 Pro updated Mobile device: None TensorFlow installed from: pip install TensorFlow version: 1.11.0 Python Version: 3.6.6 Bazel version: not installed CUDA/cuDNN version: CUDA 9.0, cuDNN 8.0 GPU model and memory: GF-GTX970 STRIX Exact command to reproduce: pip install tensorflow pip install tensorflow-gpu python import tensorflow as tf

    Problem

    I have had this error consistently even after trying to downgrade to older versions of CUDA tool, cuDNN, python, tensorflow and tensorflow-gpu. I have updated my enviornment variables. I have installed Visual C++ Redistributable Update. I have read and tried to follow the solutions from other similar issues (such as #10033 and #17101), but have not succeeded in fixing the problem.

    Log

    C:\Users\user>python Python 3.6.6 (v3.6.6:4cf1f54eb7, Jun 27 2018, 03:37:03) [MSC v.1900 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. <> import tensorflow as tf Traceback (most recent call last): File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in from tensorflow.python.pywrap_tensorflow_internal import * File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in _pywrap_tensorflow_internal = swig_import_helper() File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\imp.py", line 243, in load_module return load_dynamic(name, filename, file) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\imp.py", line 343, in load_dynamic return _load(spec) ImportError: DLL load failed: The specified module could not be found.

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last): File "", line 1, in File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_init_.py", line 22, in from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python_init_.py", line 49, in from tensorflow.python import pywrap_tensorflow File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 74, in raise ImportError(msg) ImportError: Traceback (most recent call last): File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in from tensorflow.python.pywrap_tensorflow_internal import * File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in _pywrap_tensorflow_internal = swig_import_helper() File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\imp.py", line 243, in load_module return load_dynamic(name, filename, file) File "C:\Users\user\AppData\Local\Programs\Python\Python36\lib\imp.py", line 343, in load_dynamic return _load(spec) ImportError: DLL load failed: The specified module could not be found.

  • Windows Support and Documentation

    Windows Support and Documentation

    I was excited to see tensorflow, but as many other users, we are on Windows, would be nice to see this support happen. Will you accept Windows port contributions?

    In the meantime, Microsoft recently released their Deep Learning toolkit which scales on multiple machines with GPUs for both Linux and Windows. https://github.com/Microsoft/CNTK

  • Upgrade to CuDNN 7 and CUDA 9

    Upgrade to CuDNN 7 and CUDA 9

    System information

    • Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
    • OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows Server 2012
    • TensorFlow installed from (source or binary): binary
    • TensorFlow version (use command below): 1.3.0-rc1
    • Python version: 3.5.2
    • Bazel version (if compiling from source): N/A
    • CUDA/cuDNN version: CUDA V8.0.44, CuDNN 6.0
    • GPU model and memory: Nvidia GeForce GTX 1080 Ti, 11 GB
    • Exact command to reproduce: N/A

    Describe the problem

    Please upgrade TensorFlow to support CUDA 9 and CuDNN 7. Nvidia claims this will provide a 2x performance boost on Pascal GPUs.

  • Windows C++ tensorflow_cc.dll has overlapping memory address between string gpu options for

    Windows C++ tensorflow_cc.dll has overlapping memory address between string gpu options for "allocator type" and "visible device list"

    Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template

    System information

    • Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
    • OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
    • Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: NA
    • TensorFlow installed from (source or binary): source
    • TensorFlow version (use command below): 1.12.0 branched from 5b900cfe4b3b848f577315a0dde09a729f770e95
    • Python version: NA
    • Bazel version (if compiling from source): 0.19.2
    • GCC/Compiler version (if compiling from source): MSVC 2015
    • CUDA/cuDNN version: 10.0.130, 9.2.148
    • GPU model and memory: NVIDIA GP100 16Gb

    You can collect some of this information using our environment capture script You can also obtain the TensorFlow version with: NA

    Describe the current behavior

    I am creating as session as follows adapted from original code

       std::unique_ptr<tensorflow::Session>* session;
       tensorflow::SessionOptions options;
       tensorflow::ConfigProto* config = &options.config;
       float fraction =0.8;
       int whichGPU = 0;
       int cuda_device_count=1;
       tensorflow::GraphDef graph_def;
       tensorflow::status = tensorflow::ReadBinaryProto(tensorflow::Env::Default(), "C:\\\models\\graph.pb", &graph_def);
       auto* device_count = options.config.mutable_device_count();
       device_count->insert({ "GPU", cuda_device_count });
       device_count->insert({ "CPU", 1 });
       options.config.mutable_gpu_options()->set_per_process_gpu_memory_fraction(fraction);
       options.config.mutable_gpu_options()->set_visible_device_list(std::to_string(whichGPU));
       session->reset(tensorflow::NewSession(options));
      (*session)->Create(graph_def);
    

    which results in

        70 2020-05-12 09:41:28.214176: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] 
        Found device 0 with properties: 
       71 name: Quadro GP100 major: 6 minor: 0 memoryClockRate(GHz): 1.4425
       72 pciBusID: 0000:01:00.0
       73 totalMemory: 16.00GiB freeMemory: 13.28GiB
       74 2020-05-12 09:41:28.215329: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] 
    Adding visible gpu devices: 0
       75 2020-05-12 09:41:28.952392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
       76 2020-05-12 09:41:28.952785: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 
       77 2020-05-12 09:41:28.953095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N 
        78 2020-05-12 09:41:28.953962: E tensorflow/core/common_runtime/gpu/gpu_process_state.cc:106] Invalid allocator type: 0
       79 2020-05-12 09:41:28.954425: E tensorflow/core/common_runtime/session.cc:64] Failed to create session: Internal: Failed to get memory allocator for TF GPU 0 with 6899999744 bytes of memory.
    

    Describe the expected behavior

    Session is created and runs on GPU 0 only using only 80% of available memory

    Standalone code to reproduce the issue

    #include "tensorflow/core/protobuf/control_flow.pb.h"
    #include "tensorflow/core/protobuf/config.pb.h"
    #include <iostream>
    
    int main() {
      tensorflow::GPUOptions gpu_options;
    
      gpu_options.set_visible_device_list("0");
    
      std::cout << "allocator_type " << gpu_options.allocator_type() << std::endl; //print 0
    
    }
    

    Other info / logs

    Please see the following issues https://github.com/tensorflow/tensorflow/issues/16291 https://github.com/fo40225/tensorflow-windows-wheel/issues/39

    I have built my tensorflow.dll as follows:

    $ENV:USE_BAZEL_VERSION="0.19.2" $ENV:PYTHON_BIN_PATH=C:\ProgramData\Anaconda3\python.exe $ENV:Path += ";C:\msys64\usr\bin" $ENV:Path += ";C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\bin" $ENV:Path += ";C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2\extras\CUPTI\libx64" $ENV:Path += ";C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\cudnn-9.2-windows10-x64-v7.5.0.56\cuda\bin" $ENV:BAZEL_SH = "C:\msys64\usr\bin\bash.exe" $ENV:CUDA_TOOLKIT_PATH="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.2" $ENV:TF_CUDA_VERSION="9.2" $ENV:CUDNN_INSTALL_PATH="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\cudnn-9.2-windows10-x64-v7.5.0.56\cuda" $ENV:TF_CUDNN_VERSION="7" $ENV:TF_NCCL_VERSION="1" $ENV:TF_CUDA_COMPUTE_CAPABILITIES="3.5,3.7,5.0,5.2,6.0,6.1" $ENV:TF_CUDA_CLANG="0" $ENV:TF_NEED_CUDA="1" $ENV:TF_NEED_ROCM="0" $ENV:TF_NEED_OPENCL_SYCL="0"

    $params = "configure.py","" Remove-Item -Recurse -Force "C:\Windows\system32\config\systemprofile_bazel_SYSTEM\install\75b09cf1ac98c0ffb0534079b30efcc4" cmd /c "ECHO Y" | & python.exe @params bazel.exe clean --expunge bazel.exe build --copt=-nvcc_options=disable-warnings --test_tag_filters=-no_oss,-gpu,-benchmark-test,-nomac,-no_mac --announce_rc --test_timeout 300,450,1200,3600 --test_size_filters=small,medium --jobs=12 //tensorflow:libtensorflow_cc.so //tensorflow:libtensorflow_framework.so

    edits have been made to the following files:

    within

    tensorflow/BUILD

    `"//tensorflow:windows": [],`
    

    becomes

    "//tensorflow:windows": [
                "-def:" +  # This line must be directly followed by the exported_symbols_msvc.lds file
                "$(location //tensorflow:tf_exported_symbols_msvc.lds)",
            ],
    

    and within tf_cc_shared_object the function of tensorflow/BUILD

        visibility = ["//visibility:public"],
        deps = [
            "//tensorflow:tf_exported_symbols.lds",
            "//tensorflow:tf_version_script.lds",
            "//tensorflow/c:c_api",
            "//tensorflow/c/eager:c_api",
    

    becomes

        visibility = ["//visibility:public"],
        deps = [
            "//tensorflow:tf_exported_symbols.lds",
            "//tensorflow:tf_exported_symbols_msvc.lds",
            "//tensorflow:tf_version_script.lds",
            "//tensorflow/c:c_api",
            "//tensorflow/c/eager:c_api",
    

    The contents of tf_exported_symbols_msvc.lds are

    LIBRARY tensorflow_cc
    EXPORTS
        [email protected]@@[email protected]
        [email protected]@@[email protected]
        [email protected]@[email protected]@[email protected]@Z
        [email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected]@Z
        [email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@@[email protected][email protected]@[email protected]@@[email protected]@XZ
        [email protected]@[email protected]@@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@XZ
        [email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected]@@Z
        [email protected]@@[email protected]@A
        [email protected]@@[email protected]@[email protected]@[email protected]@@Z
        [email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]@@Z
        [email protected]@[email protected]@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@XZ
        [email protected]@@[email protected]@[email protected]@@Z
        [email protected]@[email protected]@[email protected]@Z
        [email protected]@[email protected]@[email protected]@Z
        [email protected]@@[email protected]
        [email protected]@[email protected]@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@XZ
        [email protected]@[email protected][email protected]@[email protected]@@[email protected]@[email protected]@[email protected]@Z
        [email protected]@@[email protected]@[email protected]@[email protected]@@@Z
        [email protected]@[email protected]@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]
        [email protected]@@[email protected]
        [email protected]@@[email protected]
        [email protected]@@[email protected]@[email protected]@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]@@Z
        [email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@AEBAXXZ
        [email protected]@@3VSaverDefDefaultTypeInterna[email protected]@A
        [email protected]@[email protected]@[email protected]@@Z
        [email protected]@@[email protected]
        [email protected]@@[email protected]@[email protected]@[email protected]@Z
        [email protected]@[email protected]@@[email protected]@[email protected]
        [email protected]@[email protected]@@[email protected]@[email protected][email protected][email protected]@@@Z
        [email protected]@[email protected]@AEAAXXZ
        [email protected]@@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected]@Z
        [email protected][email protected]@[email protected]@@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected]
        [email protected]@@[email protected]
        [email protected]@@[email protected]
        [email protected]@@[email protected]
        [email protected]@[email protected]@[email protected]@@Z
        [email protected]@@[email protected]
        [email protected]@@[email protected]@@Z
        [email protected]@@3QEBDEB
        [email protected]@@3QEBDEB
        [email protected]@@3QEBDEB
        [email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@@2QBDB
        [email protected]@[email protected]@@[email protected]@[email protected][email protected][email protected]@@@Z
        [email protected]@[email protected]@@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected]@Z
        [email protected]@@[email protected]
        [email protected]@[email protected]@[email protected]@Z
        [email protected]@@[email protected]
        [email protected]@@[email protected]
        [email protected]@@[email protected]@[email protected]@[email protected]@Z
        [email protected]@[email protected]@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@XZ
        [email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@AEAAXXZ
        [email protected]@[email protected]@@[email protected][email protected]@[email protected]@@[email protected]@XZ
        [email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected]@Z
        [email protected]@@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@XZ
        [email protected]@@[email protected]
        [email protected]@[email protected]@[email protected]
        [email protected]@@[email protected]
        [email protected]@[email protected]@[email protected]
        [email protected]@@[email protected]
        [email protected]@@[email protected]
        [email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@AEBAXXZ
        [email protected]@[email protected]@[email protected]@@Z
        [email protected]@[email protected]@[email protected]@[email protected]@Z
        [email protected]@@[email protected]@A
        [email protected][email protected]@[email protected]@@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@XZ
        [email protected]@[email protected]@[email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]@[email protected]
        [email protected]@[email protected]@[email protected]@Z
        [email protected]@@[email protected]
        [email protected]@[email protected]@[email protected]@[email protected]@@[email protected]@@[email protected]@Z
        [email protected]@@[email protected]@@Z
        [email protected]@[email protected]@@[email protected][email protected][email protected]@[email protected]@[email protected]@[email protected]@[email protected]@@[email protected]
    

    As documented by https://github.com/tensorflow/tensorflow/issues/22047#issuecomment-421452033

    My software is linked against libprotobuf.lib from https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.0.tar.gz

    built as

    cmake -G "Visual Studio 14 2015 Win64"  .. -DCMAKE_INSTALL_PREFIX="%current%\protobuf-3.6.0" -Dprotobuf_BUILD_TESTS=OFF -Dprotobuf_BUILD_SHARED_LIBS=ON -Dprotobuf_MSVC_STATIC_RUNTIME=OFF
    cmake --build . --target install --config Release -- /maxcpucount:12
    

    I also tried editing tensorflow\tf_version_script.lds to include

    *protobuf*
    

    I also tried the TF_EXPORT macro from #include "tensorflow/core/platform/macros.h"

    in tensorflow/core/public/session_options.h and tensorflow/core/common_runtime/session_options.cc

    as suggested by https://github.com/sitting-duck/stuff/tree/master/ai/tensorflow/build_tensorflow_1.14_source_for_Windows

    Do you have any suggestions about how to make sure that

    the GPU options for allocator type and visible device list do not share the same memory but we still have a monolithic DLL under windows?

  • Quantization-Aware Training support in Keras

    Quantization-Aware Training support in Keras

    System information

    • TensorFlow version (you are using): 1.13.1 (but willing to use 2.0.0-alpha0 if there is a good reason)
    • Are you willing to contribute it (Yes/No): Yes (given some pointers on how to best go about it)

    Describe the feature and the current behavior/state. Currently there is no obvious way to apply tf.contrib.quantize.create_training_graph to a keras model. The keras API only allows access to the graph after it has already created a session. Attempting to modify the graph at this point does not work: https://stackoverflow.com/questions/55123417/quantization-aware-retraining-a-keras-model https://stackoverflow.com/questions/52259343/quantize-a-keras-neural-network-model

    I have also tried to create a new session after rewriting the graph, without success:

    tf.contrib.quantize.create_training_graph(input_graph=tf.keras.backend.get_session().graph, quant_delay=0)
    # create a new session after rewriting the graph
    new_session = tf.Session()
    tf.keras.backend.set_session(new_session)
    

    Results in this error when I try to fit the model:

    tensorflow.python.framework.errors_impl.FailedPreconditionError: Error while reading resource variable dense_5/bias from Container: localhost. This could mean that the variable was uninitialized. Not found: Resource localhost/dense_5/bias/class tensorflow::Var does not exist.
            [[{{node dense_5/BiasAdd/ReadVariableOp}}]]
    

    Will this change the current api? How? Probably, but in a backwards-compatible way. I imagine some kind of graph rewriting hook would probably be necessary in the tf.keras API.

    Who will benefit with this feature? Users of TF Lite / Edge TPU wishing to easily train quantized models using the keras API (which is being pushed as the new "one true API" for tensorflow).

    Any Other info. Related issue on the main keras project https://github.com/keras-team/keras/issues/11105

  • Unable to install TensorFlow on Python3.7 with pip

    Unable to install TensorFlow on Python3.7 with pip

    System information

    • Have I written custom code (as opposed to using a stock example script provided in TensorFlow): N/A
    • OS Platform and Distribution (e.g., Linux Ubuntu 16.04): macOS 10.13
    • TensorFlow installed from (source or binary): binary
    • TensorFlow version (use command below): 1.8
    • Python version: 3.7
    • Bazel version (if compiling from source): N/A
    • GCC/Compiler version (if compiling from source): N/A
    • CUDA/cuDNN version: N/A
    • GPU model and memory: N/A
    • Exact command to reproduce: pip install tensorflow

    Describe the problem

    Installing TensorFlow on Python3.7 with pip failed. Please see the failure log below.

    Source code / logs

    Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow

  • Crash: Could not create cuDNN handle when convnets are used

    Crash: Could not create cuDNN handle when convnets are used

    Tensorflow (GPU) was imported successfully, but when running a session that involves a convolutional neural network (CNN), Python crashes with the following message:

    E tensorflow/stream_executor/cuda/cuda_dnn.cc:385] could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
    E tensorflow/stream_executor/cuda/cuda_dnn.cc:352] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM
    F tensorflow/core/kernels/conv_ops.cc:605] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms) 
    

    The problem persists on any combination of CUDA toolkit 7.5/8.0 and Tensorflow installed from pip/source. Test sessions that do not use CNNs are run successfully.

    What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?

    The issue is similar to https://github.com/tensorflow/tensorflow/issues/6586, where I first commented. But since I experience the problem on a Mac, I was suggested to open a separate issue.

    Environment info

    Operating System: macOS Sierra 10.12.2 Xcode version 8.2 (8C38) (When I later tried CUDA 7.5, I installed Command Line Tools version 7.3.1 because CUDA 7.5 lacked support of the more recent compilers.) Python 3.5.2 (anaconda)

    Installed version of CUDA: tried both 8.0 (initially) and 7.5 (reported here, toolkit only -- the driver is still 8.0) Installed version of cuDNN: 5.1 (different installations according to CUDA versions) (please attach the output of ls -l /path/to/cuda/lib/libcud*):

    lrwxr-xr-x  1 root   wheel        33  5 Jan 20:33 /usr/local/cuda/lib/libcuda.1.dylib -> /usr/local/cuda/lib/libcuda.dylib
    [email protected] 1 root   wheel      8280 13 Apr  2016 /usr/local/cuda/lib/libcuda.dylib
    [email protected] 1 root   wheel        45 13 Apr  2016 /usr/local/cuda/lib/libcudadevrt.a -> /Developer/NVIDIA/CUDA-7.5/lib/libcudadevrt.a
    [email protected] 1 root   wheel        50 13 Apr  2016 /usr/local/cuda/lib/libcudart.7.5.dylib -> /Developer/NVIDIA/CUDA-7.5/lib/libcudart.7.5.dylib
    [email protected] 1 root   wheel        46 13 Apr  2016 /usr/local/cuda/lib/libcudart.dylib -> /Developer/NVIDIA/CUDA-7.5/lib/libcudart.dylib
    [email protected] 1 root   wheel        49 13 Apr  2016 /usr/local/cuda/lib/libcudart_static.a -> /Developer/NVIDIA/CUDA-7.5/lib/libcudart_static.a
    lrwxr-xr-x  1 root   wheel        16  5 Jan 17:14 /usr/local/cuda/lib/libcudnn.5 -> libcudnn.5.dylib
    [email protected] 1 ymfa   staff  58975112 10 Jun  2016 /usr/local/cuda/lib/libcudnn.5.dylib
    [email protected] 1 ymfa   staff        16 10 Jun  2016 /usr/local/cuda/lib/libcudnn.dylib -> libcudnn.5.dylib
    lrwxr-xr-x  1 root   wheel        16  5 Jan 17:14 /usr/local/cuda/lib/libcudnn5.dylib -> libcudnn.5.dylib
    [email protected] 1 ymfa   staff  56392320 10 Jun  2016 /usr/local/cuda/lib/libcudnn_static.a
    

    I tried both installing from pip and source. I first installed from binary pip package:

    1. A link to the pip package you installed: tensorflow-gpu
    2. The output from python -c "import tensorflow; print(tensorflow.__version__)". 0.12.head

    Later I installed from source (the pip package was uninstalled):

    1. The commit hash (git rev-parse HEAD) d67c09d98a576e1fbf2f3609ddb842e53890f31c

    2. The output of bazel version

      Build label: 0.4.3-homebrew Build target: bazel-out/local-opt/bin/src/main/java/com/google/devtools/build/lib/bazel/BazelServer_deploy.jar Build time: Thu Dec 22 15:20:15 2016 (1482420015) Build timestamp: 1482420015 Build timestamp as int: 1482420015

    If possible, provide a minimal reproducible example

    I made a minimal example by simplifying the network and reducing the training data to only twenty images and two classes for classification. issue.zip contains the Python code and the data. I wrote two convolutional layers because I found the network with only one convolutional layer runs without problem.

    Complete log using CUDA 7.5 and Tensorflow compiled from source

    I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcublas.7.5.dylib locally
    I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcudnn.5.dylib locally
    I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcufft.7.5.dylib locally
    I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcuda.1.dylib locally
    I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcurand.7.5.dylib locally
    W tensorflow/core/platform/cpu_feature_guard.cc:95] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
    W tensorflow/core/platform/cpu_feature_guard.cc:95] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
    W tensorflow/core/platform/cpu_feature_guard.cc:95] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
    I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:874] OS X does not support NUMA - returning NUMA node zero
    I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
    name: GeForce GT 650M
    major: 3 minor: 0 memoryClockRate (GHz) 0.9
    pciBusID 0000:01:00.0
    Total memory: 1023.69MiB
    Free memory: 740.18MiB
    I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
    I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y 
    I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 650M, pci bus id: 0000:01:00.0)
    E tensorflow/stream_executor/cuda/cuda_dnn.cc:385] could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
    E tensorflow/stream_executor/cuda/cuda_dnn.cc:352] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM
    F tensorflow/core/kernels/conv_ops.cc:605] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms) 
    

    Complete log using CUDA 8.0 and Tensorflow installed from pip

    I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.dylib locally
    I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.dylib locally
    I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.dylib locally
    I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.1.dylib locally
    I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.dylib locally
    I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] OS X does not support NUMA - returning NUMA node zero
    I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
    name: GeForce GT 650M
    major: 3 minor: 0 memoryClockRate (GHz) 0.9
    pciBusID 0000:01:00.0
    Total memory: 1023.69MiB
    Free memory: 590.00MiB
    I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
    I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y 
    I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 650M, pci bus id: 0000:01:00.0)
    E tensorflow/stream_executor/cuda/cuda_dnn.cc:385] could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED
    E tensorflow/stream_executor/cuda/cuda_dnn.cc:392] error retrieving driver version: Invalid argument: expected %d.%d or %d.%d.%d form for driver version; got ""
    E tensorflow/stream_executor/cuda/cuda_dnn.cc:352] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM
    F tensorflow/core/kernels/conv_ops.cc:532] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms)
    
  • ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory

    ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory

    I installed tf-nightly build and I get the following error on import of tensorflow. ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory.

    If I check for cuda 9, I get the following:

    ldconfig -v
    /usr/local/cuda-8.0/targets/x86_64-linux/lib:
    	libnvgraph.so.8.0 -> libnvgraph.so.8.0.61
    	libnppicom.so.8.0 -> libnppicom.so.8.0.61
    	libnppial.so.8.0 -> libnppial.so.8.0.61
    	libcufftw.so.8.0 -> libcufftw.so.8.0.61
    	libcufft.so.8.0 -> libcufft.so.8.0.61
    	libnppif.so.8.0 -> libnppif.so.8.0.61
    	libcublas.so.8.0 -> libcublas.so.8.0.88
    	libnvblas.so.8.0 -> libnvblas.so.8.0.88
    	libnppi.so.8.0 -> libnppi.so.8.0.61
    	libcusolver.so.8.0 -> libcusolver.so.8.0.61
    	libnppidei.so.8.0 -> libnppidei.so.8.0.61
    	libnvrtc-builtins.so.8.0 -> libnvrtc-builtins.so.8.0.61
    	libnvrtc.so.8.0 -> libnvrtc.so.8.0.61
    	libnpps.so.8.0 -> libnpps.so.8.0.61
    	libcuinj64.so.8.0 -> libcuinj64.so.8.0.61
    	libnppig.so.8.0 -> libnppig.so.8.0.61
    	libOpenCL.so.1 -> libOpenCL.so.1.0.0
    	libnppicc.so.8.0 -> libnppicc.so.8.0.61
    	libnppist.so.8.0 -> libnppist.so.8.0.61
    	libnppisu.so.8.0 -> libnppisu.so.8.0.61
    	libnppim.so.8.0 -> libnppim.so.8.0.61
    	libcurand.so.8.0 -> libcurand.so.8.0.61
    	libcudart.so.8.0 -> libcudart.so.8.0.61
    	libnvToolsExt.so.1 -> libnvToolsExt.so.1.0.0
    	libnppitc.so.8.0 -> libnppitc.so.8.0.61
    	libnppc.so.8.0 -> libnppc.so.8.0.61
    	libcusparse.so.8.0 -> libcusparse.so.8.0.61
    /usr/local/cuda-9.1/targets/x86_64-linux/lib:
    	libnppicc.so.9.1 -> libnppicc.so.9.1.85
    	libnppisu.so.9.1 -> libnppisu.so.9.1.85
    	libcufftw.so.9.1 -> libcufftw.so.9.1.85
    	libcufft.so.9.1 -> libcufft.so.9.1.85
    	libnppial.so.9.1 -> libnppial.so.9.1.85
    	libnppist.so.9.1 -> libnppist.so.9.1.85
    	libcublas.so.9.1 -> libcublas.so.9.1.85
    	libnvblas.so.9.1 -> libnvblas.so.9.1.85
    	libnppitc.so.9.1 -> libnppitc.so.9.1.85
    	libcusolver.so.9.1 -> libcusolver.so.9.1.85
    	libnvrtc.so.9.1 -> libnvrtc.so.9.1.85
    	libnvrtc-builtins.so.9.1 -> libnvrtc-builtins.so.9.1.85
    	libnppidei.so.9.1 -> libnppidei.so.9.1.85
    	libOpenCL.so.1 -> libOpenCL.so.1.0.0
    	libnppig.so.9.1 -> libnppig.so.9.1.85
    	libnppc.so.9.1 -> libnppc.so.9.1.85
    	libcudart.so.9.1 -> libcudart.so.9.1.85
    	libnvToolsExt.so.1 -> libnvToolsExt.so.1.0.0
    	libnvgraph.so.9.1 -> libnvgraph.so.9.1.85
    	libnppif.so.9.1 -> libnppif.so.9.1.85
    	libcusparse.so.9.1 -> libcusparse.so.9.1.85
    	libaccinj64.so.9.1 -> libaccinj64.so.9.1.85
    	libcuinj64.so.9.1 -> libcuinj64.so.9.1.85
    	libnppim.so.9.1 -> libnppim.so.9.1.85
    	libnppicom.so.9.1 -> libnppicom.so.9.1.85
    	libnpps.so.9.1 -> libnpps.so.9.1.85
    	libcurand.so.9.1 -> libcurand.so.9.1.85
    

    I that due to a name mismatch. libcublas.so.9.0 =! libcublas.so.9.1? And if so how can we overcome this?

  • [Question&Error] Is there detection model like a SSD-Mobile-net in tensorflow-lite?

    [Question&Error] Is there detection model like a SSD-Mobile-net in tensorflow-lite?

    HI.

    Developing an android application using tensorflow-lite.

    https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/g3doc/models.md Not found detection model.

    Also, I try to convert SSD-Inceptionv2 using tensorflow-lite-API. But there seems to be a problem.

    ##Command

    
    bazel run --config=opt --copt=-msse4.1 --copt=-msse4.2 \
      //tensorflow/contrib/lite/toco:toco -- \
      --input_file=/home/danshin/tensorflow_lite/lite_model/fire_incpetion_v2.pb \
      --output_file=/home/danshin/tensorflow_lite/lite_model/fire_inception_v2.lite \
      --input_format=TENSORFLOW_GRAPHDEF \
      --output_format=TFLITE \
      --inference_type=FLOAT \
      --input_shape=1,300,300,3 \
      --input_array=image_tensor \
      --output_array={detection_boxes,detection_scores,detection_classes,num_detections}
    

    ##Error code

    
    2017-12-26 14:59:25.159220: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before general graph transformations: 2029 operators, 3459 arrays (0 quantized)
    2017-12-26 14:59:25.251633: F tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc:95] Check failed: other_op->type == OperatorType::kTensorFlowMerge 
    

    The fire_inception_v2 file is created, but its size is zero bytes. What is a problem?

    also, please let me know what's the best way to deploy custom model for object detection?

    Somebody help me plz!.

    thank you.

  • compatible Issue between CUDA version and tensorflow version

    compatible Issue between CUDA version and tensorflow version

    Hello, I have a question about the CUDA version and tensor flow version. I have looked the compatible from https://github.com/tensorflow/docs/blob/master/site/en/install/source.md#gpu The setup for my GPU is: image with CUDA version: 10.2

    When I try to run the ML code with tensor flow version 1.14.0, it is Find. But when I run the code with tensor flow version of 1.15.1, it raise error like this:

    tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found. (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node conv1d_1/convolution}}]] [[loss/mul/_131]] (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node conv1d_1/convolution}}]] 0 successful operations. 0 derived errors ignored. E0517 07:32:37.654887 140167361226560 logger.py:55] 2 root error(s) found. (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node conv1d_1/convolution}}]] [[loss/mul/_131]] (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node conv1d_1/convolution}}]] 0 successful operations. 0 derived errors ignored. Traceback (most recent call last): File "/home/timhon/amlml2/aml_prediction/project/classes/Evaluator/trainer.py", line 99, in make_prediction best_model, best_param_df = self.train_model(**train_params) File "/home/timhon/amlml2/aml_prediction/project/classes/Evaluator/trainer.py", line 166, in train_model mclass.getoptimized(paramslist, traindata, evaldata) File "/home/timhon/amlml2/aml_prediction/project/classes/Modelling/nlpmodels.py", line 248, in getoptimized return super().getoptimized(paramslist, traindata, evaldata) File "/home/timhon/amlml2/aml_prediction/project/classes/Modelling/basemodel.py", line 82, in getoptimized results = self.validate(params, traindata, evaldata) File "/home/timhon/amlml2/aml_prediction/project/classes/Modelling/nlpmodels.py", line 235, in validate callbacks=[early_stopping, history], verbose=1, validation_split=0.2) File "/home/timhon/.local/lib/python3.6/site-packages/keras/engine/training.py", line 1039, in fit validation_steps=validation_steps) File "/home/timhon/.local/lib/python3.6/site-packages/keras/engine/training_arrays.py", line 199, in fit_loop outs = f(ins_batch) File "/home/timhon/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2715, in call return self._call(inputs) File "/home/timhon/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2675, in _call fetched = self._callable_fn(*array_vals) File "/home/timhon/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py", line 1472, in call run_metadata_ptr) tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found. (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node conv1d_1/convolution}}]] [[loss/mul/_131]] (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node conv1d_1/convolution}}]] 0 successful operations. 0 derived errors ignored.

    is there are anyone know the error? Remarks: to test if the problem is coming from my Python-scripts, I also try to run the SAME script in different computer, with CUDA version of 11.2, and it works!!!! Hence, I think we can ignore the issue from my scripts...at less it is working in other machines

  • Add data init funcs for TFLite Swift API

    Add data init funcs for TFLite Swift API

    As per #56144 it'd be nice if Interpreters could be initialized from Data instead of file paths like in the Android API. I added functions for this feature.

    I'm having trouble building this locally so I'm PR'ing it in hopes that I can build via PR. Locally, I see many messages like

    error: output 'tensorflow/lite/swift/TensorFlowLiteAllDelegates_objs/Sources/CoreMLDelegate.swift.partial_swiftmodule' was not created
    Compiling Swift module //tensorflow/lite/swift:TensorFlowLiteAllDelegates failed: not all outputs were created or valid
    

    from Bazel. I'm using XCode 13.3.1 and Bazel 5.1.1

  • Data init API for TFLite Swift

    Data init API for TFLite Swift

    Click to expand!

    Issue Type

    Feature Request

    Source

    source

    Tensorflow Version

    2.8+

    Custom Code

    No

    OS Platform and Distribution

    No response

    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 Behaviour?

    The current Swift API only has `init` functions from files on disk unlike the Java (Android) API which has a byte buffer initializer. It'd be convenient if the Swift API could initialize `Interpreters` from `Data`.
    

    Standalone code to reproduce the issue

    No code. This is a feature request
    

    Relevant log output

    No response

  • TensorFlow core operator ExtractImagePatches , not included and supported

    TensorFlow core operator ExtractImagePatches , not included and supported

    Click to expand!

    Issue Type

    Support

    Source

    source

    Tensorflow Version

    tf 2.9

    Custom Code

    Yes

    OS Platform and Distribution

    Windows 11

    Mobile device

    No response

    Python version

    3.10

    Bazel version

    No response

    GCC/Compiler version

    No response

    CUDA/cuDNN version

    No response

    GPU model and memory

    RTX 1660Ti and 16 GB

    Current Behaviour?

    I made a custom  layer which uses ExtractImagePatches TensorFlow operator, but it was not supported by tflite (but listed as supported). I tried to enable the op manually but was unable to find .cc and .h file. It would be great if there is a solution to this problem, since my research depends on it.
    
    RuntimeError: Failed to initialize op resolver for calibration:
    There are unresolved custom ops: [ExtractImagePatches]Encountered unresolved custom op: ExtractImagePatches.
    See instructions: https://www.tensorflow.org/lite/guide/ops_customNode number 0 (ExtractImagePatches) failed to prepare.
    

    Standalone code to reproduce the issue

    import tensorflow as tf
    import matplotlib.pyplot as plt
    import numpy as np
    from keras.utils import np_utils
    from tensorflow.python.keras import activations
    from sklearn.model_selection import KFold
    from tensorflow.python.keras.callbacks import EarlyStopping
    def myconv2d(ix, w, padding):
       # filter shape: [filter_height, filter_width, in_channels, out_channels]
       # flatten filters
       filter_height = int(w.shape[0])
       filter_width = int(w.shape[1])
       in_channels = int(w.shape[2])
       out_channels = int(w.shape[3])
       ix_height = int(ix.shape[1])
       ix_width = int(ix.shape[2])
       ix_channels = int(ix.shape[3])
       filter_shape = [filter_height, filter_width, in_channels, out_channels]
       flat_w = tf.reshape(w, [filter_height * filter_width * in_channels, out_channels])
       patches = tf.image.extract_patches(
           ix,
           sizes=[1, filter_height, filter_width, 1],
           strides=[1, 1, 1, 1],
           rates=[1, 1, 1, 1],
           padding= padding
       )
       patches_reshaped = tf.reshape(patches, [-1, ix_height, ix_width, filter_height * filter_width * ix_channels])
       feature_maps = []
       for i in range(out_channels):
           feature_map = tf.reduce_sum(tf.multiply(flat_w[:, i], patches_reshaped), axis=3, keepdims=True)
           feature_maps.append(feature_map)
       features = tf.concat(feature_maps, axis=3)
       return features
    
    class MyConv2D(tf.keras.layers.Layer):
        def __init__(self, filters, kernel_size,padding='SAME', **kwargs):
            self.filters = filters
            self.kernel_size = kernel_size
            self.padding = padding
            #self.units= units
            super(MyConv2D, self).__init__(**kwargs)
    
        def get_config(self):
            config = super().get_config()
            config.update({
                "filters": self.filters,
                "kernel_size": self.kernel_size,
                "padding" : self.padding,
            })
            return config
    
        def build(self, input_shape):
            # only have a 3x3 kernel
            shape = self.kernel_size + (input_shape[-1], self.filters)
            self.kernel = self.add_weight(name='kernel', shape=shape,
                                          initializer='glorot_uniform', trainable=True)
            self.b = self.add_weight(
                name="bias", shape=(self.filters,), initializer="random_normal", trainable=True
            )
            super((MyConv2D, self).build(input_shape))
    
        def call(self, inputs):
            result = myconv2d(inputs, self.kernel, self.padding) + self.b
            return result
    
        def compute_output_shape(self, input_shape):
            return input_shape[:-1] + (self.filters,)
    
    
    def load_dataset():
    	# load dataset
    	(trainX, trainY), (testX, testY) = tf.keras.datasets.mnist.load_data()
    	# reshape dataset to have a single channel
    	trainX = trainX.reshape((trainX.shape[0], 28, 28, 1))
    	testX = testX.reshape((testX.shape[0], 28, 28, 1))
    	# one hot encode target values
    	trainY = np_utils.to_categorical(trainY)
    	testY = np_utils.to_categorical(testY)
    	# convert from integers to floats
    	train_norm = trainX.astype('float32')
    	test_norm = testX.astype('float32')
    	# normalize to range 0-1
    	trainX = train_norm / 255.0
    	testX = test_norm / 255.0
    	# return normalized images
    	return trainX, trainY, testX, testY
    
    
    def create_model():
    	# creating a sequantial model
    	model = tf.keras.Sequential()
    	# adding convolution2D layer to the model of 32 filters of size 3x3
    	model.add(MyConv2D(filters=32, kernel_size=(3, 3), input_shape=(28, 28, 1)))
    	model.add(tf.keras.layers.Activation(activations.relu))
    	# adding a maxpooling 2D layer of size 2x2
    	model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
    	# adding a Flatten layer
    	model.add(tf.keras.layers.Flatten())
    	# adding Dense layer with 'relu' activation
    	model.add(tf.keras.layers.Dense(100, activation='relu'))
    	# adding Dense layer with 'softmax' activation for output
    	model.add(tf.keras.layers.Dense(10, activation='softmax'))
    	return model
    
    
    def define_model(model):
    	# compile model
    	opt = tf.keras.optimizers.Adam()
    	model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
    	return model
    
    
    if __name__ == "__main__":
    	train_images, train_labels, test_images, test_labels = load_dataset()
    
    	model = create_model()
    
    	# compile model
    	opt = tf.keras.optimizers.Adam()
    	model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
    	es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)
    	# fit model
    	history = model.fit(train_images, train_labels, epochs=50, batch_size=32,
    						validation_data=(test_images, test_labels), callbacks=[es])
    	# evaluate model
    	scores = model.evaluate(test_images, test_labels, verbose=0)
    	print("Accuracy: %.2f%%" % (scores[1] * 100))
    	# stores scores
    

    Relevant log output

    No response

  • Mix precision leads to nan loss

    Mix precision leads to nan loss

    Click to expand!

    Issue Type

    Bug

    Source

    pip install

    Tensorflow Version

    tf2.8

    Custom Code

    Yes

    OS Platform and Distribution

    windows

    Mobile device

    windows 21h2 (19044, 1706)

    Python version

    python 3.9.7

    Bazel version

    unkonwn

    GCC/Compiler version

    unknown

    CUDA/cuDNN version

    11.2

    GPU model and memory

    RTX3070/8Gb

    Problem Discription?

    Loss is nan when using mix precision API even though use (get_scaled_loss, get_unscaled_gradients) or scale loss manually.

    • Model works well in float32.
    • Mix precision API with tensorflow guide works well in my computer.
    • My model input start from Cond2D -> ... -> Resblock -> ... -> Dense -> SoftMax, it's to solve a classification problem.

    Standalone code to reproduce the issue

    type 1 (follow tensorflow guide)
    for scale in range(1,3000,30):
        with tf.GradientTape() as tape:
            y_pred = model(x_train,training = True)
            loss = tf.keras.losses.categorical_crossentropy(y_train, y_pred)
            print(tf.reduce_mean(loss))
            loss = optimizer.get_scaled_loss(loss)
            print(tf.reduce_mean(loss))
        grads = tape.gradient(loss, model.trainable_weights)
        grads = optimizer.get_unscaled_gradients(grads)
        optimizer.apply_gradients(zip(grads, model.trainable_weights))
    -----------------------------------------------------------------
    type 2 (scale loss manually)
    for scale in range(1,3000,30):
        with tf.GradientTape() as tape:
            y_pred = model(x_train,training = True)
            loss = tf.keras.losses.categorical_crossentropy(y_train, y_pred)
            loss *= scale
            print(tf.reduce_mean(loss))
        grads = tape.gradient(loss, model.trainable_weights)
        print(tf.reduce_mean(grads[0]))
        grads = [x*scale for x in grads]
        optimizer.apply_gradients(zip(grads, model.trainable_weights))
    

    Relevant log output

    type 1
    
    tf.Tensor(9.516, shape=(), dtype=float16)   # unscaled loss
    tf.Tensor(inf, shape=(), dtype=float16)     # scaled loss
    tf.Tensor(9.516, shape=(), dtype=float16)   # unscaled loss
    tf.Tensor(inf, shape=(), dtype=float16)     # scaled loss
    ...
    ... # same below
    #-------------------------------------------------------------------------
    type 2
    
    tf.Tensor(9.516, shape=(), dtype=float16)    # scaled loss
    tf.Tensor(nan, shape=(), dtype=float32)       # gradients
    tf.Tensor(961.0, shape=(), dtype=float16)    # scaled loss
    tf.Tensor(nan, shape=(), dtype=float32)       # gradients
    tf.Tensor(1913.0, shape=(), dtype=float16)   # scaled loss
    tf.Tensor(nan, shape=(), dtype=float32)        # gradients
    ...
    ...  # all gradients are nan
    
  • adding the SCF ops required for jit64 indexing

    adding the SCF ops required for jit64 indexing

    A follow up PR to enable the mlir::scf ops and compile and execute kernels within the xla_legalize_tf_no_fallback pass. This is required to compile the jit 64 bit indexing. cc: @frgossen

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Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition.

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A library for creating Artificial Neural Networks, for use in Machine Learning and Deep Learning algorithms.
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Parallel programming for everyone.
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SecMML: Secure MPC(multi-party computation) Machine Learning Framework
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Open standard for machine learning interoperability
Open standard for machine learning interoperability

Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides

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Caffe: a fast open framework for deep learning.

Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berke

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Distributed machine learning platform

Veles Distributed platform for rapid Deep learning application development Consists of: Platform - https://github.com/Samsung/veles Znicz Plugin - Neu

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A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

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A lightweight C++ machine learning library for embedded electronics and robotics.

Fido Fido is an lightweight, highly modular C++ machine learning library for embedded electronics and robotics. Fido is especially suited for robotic

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High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

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Feature Store for Machine Learning
Feature Store for Machine Learning

Overview Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production. Please see ou

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Machine Learning Platform for Kubernetes
Machine Learning Platform for Kubernetes

Reproduce, Automate, Scale your data science. Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applica

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In-situ data analyses and machine learning with OpenFOAM and Python

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In this tutorial, we will use machine learning to build a gesture recognition system that runs on a tiny microcontroller, the RP2040.
In this tutorial, we will use machine learning to build a gesture recognition system that runs on a tiny microcontroller, the RP2040.

Pico-Motion-Recognition This Repository has the code used on the 2 parts tutorial TinyML - Motion Recognition Using Raspberry Pi Pico The first part i

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