TensorFlow officially released 1.5.0, doubling the training speed on Volta GPUs/FP16
TensorFlow officially released version 1.5.0 today, supporting CUDA 9 and cuDNN 7, further speeding up. Also, starting with version 1.6, precompiled binaries will use AVX instructions, which can corrupt TFs on older CPUs. Just now, TensorFlow released the official version of 1.5.0, which many people have been waiting for for a long time. The most significant change is to support CUDA 9 and cuDNN 7, which promises to double the training speed on Volta GPUs/FP16. In addition, the Eager execution preview is available and will appeal to many beginners. The following are major changes and bug fixes for this update. Major change The precompiled binaries are now built for CUDA 9 and cuDNN 7. Starting with version 1.6, the precompiled binaries will use the AVX instructions. This can damage the TF on older CPUs. Main features and improvements Eager execution The preview version is now available. TensorFlow Lite The dev preview is now available. CUDA 9 and cuDNN 7 support are available. Accelerated Linear Algebra (XLA): Add complex64 support to the XLA compiler. Bfloat support is now being added to the XLA infrastructure. Make ClusterSpec propagation work with XLA devices. Use a decisive executive to generate an XLA diagram. Tf.contrib: Tf.contrib.distributions: Add tf.contrib.distributions.Autoregressive. Make the tf.contrib.distributions QuadratureCompound class support batch processing Infer the tf.contrib.distributions.RelaxedOneHotCategorical dtype from the parameters. The tf.contrib.distributions orthogonal family is parameterized as quadrature_grid_and_prob vs quadrature_degree. Add auto_correlation to tf.contrib.distributions Add tf.contrib.bayesflow.layers, a collection of probabilistic (neural) layers. Add tf.contrib.bayesflow.halton_sequence. Add tf.contrib.data.make_saveable_from_iterator. Add tf.contrib.data.shuffle_and_repeat. Add a new custom transform: tf.contrib.data.scan(). Tf.contrib.distributions.bijectors: Add tf.contrib.distributions.bijectors.MaskedAutoregressiveFlow. Add tf.contrib.distributions.bijectors.Permute. Add tf.contrib.distributions.bijectors.Gumbel. Add tf.contrib.distributions.bijectors.Reshape. Shape prediction is supported (ie, a shape containing -1) in a Reshape bijector. Add streaming_precision_recall_at_equal_thresholds, streaming precision calculation method and O(num_thresholds + predicted size) time and space complexity. Change the default behavior of RunConfig without setting a random seed so that the random behavior is independent and random on the distributed worker. Looking forward to this, the training effect is generally improved. Models that rely on determinism should explicitly set up a random seed. Replace the implementation of tf.flags with absl.flags. Added support for CUBLAS_TENSOR_OP_MATH in fp16 GEMM Add support for CUDA on NVIDIA Tegra devices Bug fixes and other changes Documentation update: Note TensorFlow can only be installed on a 64-bit machine. Added a short document explaining how Estimators can save checkpoints. Add documentation for operations supported by the tf2xla bridge. Fix small typos in the SpaceToDepth and DepthToSpace documents. The documentation comments have been updated in mfcc_mel_filterbank.h and mfcc.h to indicate that the input field is the square of the amplitude spectrum and the weight is done on the linear magnitude spectrum (the square of the input). Change the tf.contrib.distributions docstring example to use the tfd alias instead of ds, bs. Fix docstring typos in tf.distributions.bijectors.Bijector. Tf.assert_equal no longer raises a ValueError. Now propose an InvalidArgumentError. Update the Getting Started documentation and API introduction. Google Cloud Storage (GCS): Add a user space DNS cache for the GCS client. Custom request timeout for the GCS file system. Improve the GCS file system cache. Bug fix: Fixed an issue where partitioned integer variables got the wrong shape. Fixed correctness bug in Adadelta's CPU and GPU implementation. Fixed an error in import_meta_graph when processing partition variables. Warning: This may corrupt the graphical load checkpoint of a partition variable saved after using import_meta_graph with a non-empty import_scope parameter. Fixed an error in the offline debugger that prevented viewing events. Add the WorkerService.DeleteWorkerSession method to the gRPC interface to fix memory leaks. Make sure the primary and worker servers are running the same version of TensorFlow to avoid compatibility issues. Fix bug in the peephole implementation of the Peephole of the BlockLSTM unit. Fix the bug by converting dtype's log_det_jacobian to match log_prob in TransformedDistribution. Fix the error that import_meta_graph handles partition variables, ensuring that tf.distributions.Multinomial does not underflow in log_prob. Prior to this change, all partitions of the integer variable were initialized with the shape of the unpartitioned variable; they were properly initialized after this change. other Add the necessary shape util support for bfloat16. Add a method to run ops using the step function of MonitoredSession. Add the DenseFlipout probability layer. There is a new flag for ignore_live_threads when training. If set to True, it will ignore the thread that was still running when the infrastructure was removed, after successfully completing the training, instead of throwing a RuntimeError. Re-standardize DenseVariational as a simple template layer for other probabilities. Tf.data now supports the tf.SparseTensor component in dataset elements. It is now possible to traverse Tensors. Allows the SparseSegmentReduction operation to lack the segment ID. Modify the custom export strategy to illustrate multidimensional sparse floating segmentation. Conv2D, Conv2DBackpropInput, Conv2DBackpropFilter now support any extensions with GPU and cuDNNv6 support. The estimator now supports data sets: input_fn can return data sets instead of tensors. Add RevBlock, which is an efficient memory implementation of the reversible residual layer. Reduce internal fragmentation of BFCAllocator. Add cross_entropy and kl_divergence to tf.distributions.Distribution. Add the tf.nn.softmax_cross_entropy_with_logits_v2 wrt tag with backpropagation enabled. The GPU backend now compiles the generated PTX using ptxas. The protocol buffer dump for BufferAssignment is now deterministic. Change the embedding operation to use the parallel version of DynamicStitch. Added support for sparse multidimensional feature columns. Speed ​​up the case of sparse floating-point columns with only 1 value. Sparse floating partitioning is allowed to support multi-valued feature columns. Add quantiles to tf.distributions.TransformedDistribution. Added NCHW_VECT_C support for tf.depth_to_space on the GPU. Added NCHW_VECT_C support for tf.space_to_depth on the GPU. API changes Rename the SqueezeDims property to Axis in the C++ API for Squeeze operations. Stream :: BlockHostUntilDone now returns Status instead of bool. Minor Refactoring: Move statistical files from random to common and remove random.
Description of Zipper Braided Sleeve
Whether your problem is getting past large connectors or not wanting to disconnect complex cable runs, this zipper cable sleeve side-entry braided wrap is an ideal cable management solution. Zip up cable sleeving is great for wire installations in which only a small portion of the bundle needs to be concealed, and is easily removed and relocated at your convenience.
Our expandable braided zipper sleeving offers exceptional cable protection, but is really used to tidy up exposed cable runs. TV, computer and phone setups can all have a clean and organized cables instead of those ugly cable nests.
Automotive and marine applications such as aftermarket speaker systems, GPS or other component accessories that may need cable organization can benefit from this braided sleeving.
Braided Cable Sleeves,Zip Braided Sleeve For Cable ,Expandable Braided Cable Sleeves,Zipper Cable Sleeves Shenzhen Huiyunhai Tech.Co.,Ltd , https://www.hyhbraidedsleeve.com