submitted by /u/dirtygrip
[visit reddit] [comments]
NVIDIA CUDA-X AI are deep learning libraries for researchers and software developers to build high performance GPU-accelerated applications for conversational AI, recommendation systems and computer vision.
NVIDIA CUDA-X AI are deep learning libraries for researchers and software developers to build high performance GPU-accelerated applications for conversational AI, recommendation systems and computer vision. CUDA-X AI libraries deliver world leading performance for both training and inference across industry benchmarks such as MLPerf.
Learn what’s new in the latest releases of CUDA-X AI libraries and NGC.
Refer to each package’s release notes in documentation for additional information.
cuDNN 8.1
The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. This version of cuDNN includes:
- Support for BFloat16 for CNNs on NVIDIA Ampere architecture GPUs.
- New and easy C++ front-end API available in open source, wraps flexible v8 backend C API.
- Flexibly fuse operators such as convolutions, point-wise operations and reductions to speed up CNNs.
- New optimizations for computer vision, speech, and natural language understanding networks.
TensorRT 7.2
NVIDIA TensorRT is a platform for high-performance deep learning inference. This version of TensorRT includes:
- New debugging APIs – ONNX Graphsurgeon, Polygraphy, and Pytorch Quantization toolkit
- Support for Python 3.8
In addition this version includes several bug fixes and documentation upgrades.
Triton Inference Server 2.6
Triton is an open source inference serving software designed to maximize performance and simplify production deployment at scale. This version of Triton includes:
- Alpha version of Windows build, which supports gRPC and TensorRT backend
- Initial release of Model Analyzer, which is a tool that helps users select the optimal model configuration that maximizes performance in Triton.
- Support for Ubuntu 20.04 – Triton provides support for the latest version of Ubuntu, which comes with additional security updates.
- Native support in DeepStream – Triton on DeepStream can run inference on video analytics workflows on the edge or on the cloud with Kubernetes.
NGC Container Registry
NGC, the hub for GPU-optimized AI/ML/HPC application containers, models and SDKs that simplifies software development and deployment so users can achieve faster time to solution. This month’s updates include:
- NGC catalog in the AWS Marketplace – users can now pull the software directly from the AWS portal
- Containers for latest versions of NVIDIA AI software including Triton Inference Server, TensorRT, and deep learning frameworks such as PyTorch
DALI 0.30
The NVIDIA Data Loading Library (DALI) is a portable, open-source GPU-accelerated library for decoding and augmenting images and videos to accelerate deep learning applications. This version of DALI includes:
- New easy to use functional API. This is an experimental feature.
- DALI integration with Triton Inference Server to accelerate inference pipelines. DALI pipelines can now be run within Triton on the server side.
- New Jupyter Notebooks: Geometric Transform and Reductions
- New and improved operators for 3D/volumetric data and video processing.
NVJPEG2000 0.1
nvJPEG2000 is a new library for GPU-accelerated JPEG2000 image decoding. This version of nvJPEG2000 includes:
- Support for Linux and Windows operating systems
- Up to 4x faster lossless decoding for 5-3 wavelet decoding and upto 7x faster lossy decoding for 9-7 wavelet transform
- Bitstreams with multiple tiles are now supported.
Categories
Recognition of a text type
Hello all !
Is it possible to train tensorflow to recognize certain data in a text such as a city, brand names, etc.?
I’m just starting out, do you have good resources for this kind of word processing?
Thank you for your answers 😉
submitted by /u/Romaixn
[visit reddit] [comments]
|
submitted by /u/edthyme [visit reddit] [comments] |
Categories
Numpy-related error when building model
I am using tensorflow 2.4.1 with numpy 1.20.0, and I am trying to create a model using LSTM.
model = Sequential() model.add(LSTM(256, input_shape=(1, 66), return_sequences=True ))
Adding that LSTM layer gives me this error:
Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/training/tracking/base.py", line 517, in _method_wrapper result = method(self, *args, **kwargs) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py", line 208, in add layer(x) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 660, in __call__ return super(RNN, self).__call__(inputs, **kwargs) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 951, in __call__ return self._functional_construction_call(inputs, args, kwargs, File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 1090, in _functional_construction_call outputs = self._keras_tensor_symbolic_call( File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 822, in _keras_tensor_symbolic_call return self._infer_output_signature(inputs, args, kwargs, input_masks) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 863, in _infer_output_signature outputs = call_fn(inputs, *args, **kwargs) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent_v2.py", line 1157, in call inputs, initial_state, _ = self._process_inputs(inputs, initial_state, None) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 859, in _process_inputs initial_state = self.get_initial_state(inputs) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 642, in get_initial_state init_state = get_initial_state_fn( File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 2506, in get_initial_state return list(_generate_zero_filled_state_for_cell( File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 2987, in _generate_zero_filled_state_for_cell return _generate_zero_filled_state(batch_size, cell.state_size, dtype) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 3003, in _generate_zero_filled_state return nest.map_structure(create_zeros, state_size) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/util/nest.py", line 659, in map_structure structure[0], [func(*x) for x in entries], File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/util/nest.py", line 659, in <listcomp> structure[0], [func(*x) for x in entries], File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 3000, in create_zeros return array_ops.zeros(init_state_size, dtype=dtype) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper return target(*args, **kwargs) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py", line 2819, in wrapped tensor = fun(*args, **kwargs) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py", line 2868, in zeros output = _constant_if_small(zero, shape, dtype, name) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py", line 2804, in _constant_if_small if np.prod(shape) < 1000: File "<__array_function__ internals>", line 5, in prod File "/home/sakuya/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 3030, in prod return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out, File "/home/sakuya/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 87, in _wrapreduction return ufunc.reduce(obj, axis, dtype, out, **passkwargs) File "/home/sakuya/.local/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 852, in __array__ raise NotImplementedError( NotImplementedError: Cannot convert a symbolic Tensor (lstm_19/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported
I have no idea where I am going wrong here, but I can add the layer if I do not specify the input shape, but I need to do that.
submitted by /u/lastorder
[visit reddit] [comments]
In this technical blog post, learn how LinkedIn uses deep learning-based NLP technologies to better understand text semantics, which is key to any ranking model.
In this technical blog post, learn how LinkedIn uses deep learning-based NLP technologies to better understand text semantics, which is key to any ranking model.
Speech and natural language processing (NLP) have become the foundation for most of the AI development in the enterprise today, as textual data represents a significant portion of unstructured content. As consumer internet companies continue to improve the accuracy of conversational AI, search, and recommendation systems, there is an increasing need for processing rich text data efficiently and effectively.
However, one of the key challenges for achieving the desired accuracy lies in understanding complex semantics and underlying user intent, and effectively extracting relevant information from a variety of sources such as user queries and documents. In recent years, the rapid development of deep learning models has bolstered improvements for a variety of NLP tasks, indicating the vast potential for further improving the accuracy of search and recommender systems.
In this post, we introduce DeText, a state-of-the-art, open-source NLP framework developed at LinkedIn for text understanding, followed by a summary of the GPU-accelerated BERT assets available for you to jumpstart your NLP development.
Categories
Bayes by backprop implementation using tfp
I am trying to implement a simple bayes by backprop regression using the following tutorial
But my regressor is no where near learning. below is my model, can anyone suggest a simple implementation of the tutorial?
“`
def prior(kernel_size, bias_size, dtype=None):
n = kernel_size + bias_size
prior_model = tf.keras.Sequential([
tfp.layers.DistributionLambda(
lambda t:tfd.MultivariateNormalDiag(loc=tf.zeros(n), scale_diag=tf.ones(n))
)
])
return prior_model
def posterior(kernel_size, bias_size,dtype=None):
n = kernel_size + bias_size
posterior_model = tf.keras.Sequential([
tfp.layers.VariableLayer(tfp.layers.MultivariateNormalTriL.params_size(n),dtype=dtype),
tfp.layers.MultivariateNormalTriL(n)
])
return posterior_model
model = tf.keras.Sequential([
tfp.layers.DenseVariational(units=20,input_shape=(1,),make_prior_fn=prior, make_posterior_fn=posterior,kl_weight=1/x_train.shape[0]),
tf.keras.layers.ReLU(),
tfp.layers.DenseVariational(units=20,make_prior_fn=prior, make_posterior_fn=posterior,kl_weight=1/x_train.shape[0]),
tfp.layers.DenseVariational(units=1,make_prior_fn=prior, make_posterior_fn=posterior,kl_weight=1/x_train.shape[0])
])
model.compile(loss=tf.keras.losses.MeanSquaredError() , optimizer=tf.keras.optimizers.Adam(lr=0.001),metrics=[‘mae’])
print(model.summary())
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=500,verbose=2)
“`
submitted by /u/_tfp_beginner
[visit reddit] [comments]
I passed the tensorflow in Nov 2020. I can’t find a way to show my profile on the network. Anyone could show me how to do it please? Thanks very much!
https://developers.google.com/certification/directory/tensorflow
submitted by /u/Winnie0123
[visit reddit] [comments]
Categories
New Programmer question
|
Hello everyone. I am an M.D. I am doing medical data analysis (not image at least now), I am using Matlab . I have seen TensorFlow, I wander is it worth for me to learn it? to make regression analysis KM plots etc. ? I tried to install to give a try. I wanted to use swift for tensor flow into Xcode. I couldn’t make it work. I installed the package. I can see that on toolchains. can someone give me an idea about Tensor Flow , if it is worth to use for me ? submitted by /u/onqun |
|
AI colleagues, Indeed.com estimates average US salaries for Certified Deep Learning Engineer at $166k+. This Deep Learning with TensorFlow 2.0 Certification Training is curated with the help of experienced industry professionals as per the latest requirements & demands. This course will help you master popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. You will be working on various real-time projects like Emotion and Gender Detection, Auto Image Captioning using CNN and LSTM. Skill-based training modules include: : 1) Getting Started with TensorFlow 2.0, 2) Convolution Neural Network, 3) Regional CNN, 4) Boltzmann Machine & Autoencoder, 5) Generative Adversarial Network(GANEmotion and Gender Detection, 7) Introduction RNN and GRU, 8) LSTM,and 9) Auto Image Captioning Using CNN LSTM. Enroll today (individuals & teams are welcome): https://fxo.co/AA8u Much career success, Lawrence E. Wilson – Artificial Intelligence Academy (tinyurl.com/52s6gqb9) Certified Deep Learning Engineer – Deep Learning with TensorFlow 2.0 Certification Training submitted by /u/lwilson747 |
