{"id":16185,"date":"2018-05-23T05:28:58","date_gmt":"2018-05-23T05:28:58","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=16185"},"modified":"2021-05-14T11:00:16","modified_gmt":"2021-05-14T05:30:16","slug":"tensorflow-performance-optimization","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/tensorflow-performance-optimization\/","title":{"rendered":"TensorFlow Performance Optimization &#8211; Tips To Improve Performance"},"content":{"rendered":"<p><span style=\"font-weight: 400\">Today, in this <strong>TensorFlow<\/strong> Performance Optimization Tutorial, we\u2019ll be getting to know how to optimize the performance of our TensorFlow code. The article will help us to understand the need for optimization and the various ways of doing it. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Moreover, we will get an understanding of TensorFlow CPU memory usage and also Tensorflow GPU for optimal performance.<\/span><\/p>\n<p><span style=\"font-weight: 400\">So, l<\/span><span style=\"font-weight: 400\">et\u2019s begin with TensorFlow Performance Optimization.<\/span><\/p>\n<h2><span style=\"font-weight: 400\">Ways for TensorFlow Performance Optimization<\/span><\/h2>\n<p><span style=\"font-weight: 400\">There a variety of ways through which you can optimize your hardware tools and models. Some of the ways that we are covering during the course of this TensorFlow Performance Optimization tutorial are:<\/span><\/p>\n<div id=\"attachment_16640\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/General-best-practices-01-1.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-16640\" class=\"wp-image-16640 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/General-best-practices-01-1.jpg\" alt=\"TensorFlow Performance Optimization\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/General-best-practices-01-1.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/General-best-practices-01-1-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/General-best-practices-01-1-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/General-best-practices-01-1-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/General-best-practices-01-1-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-16640\" class=\"wp-caption-text\">Optimization of TensorFlow Performance<\/p><\/div>\n<ul>\n<li><span style=\"font-weight: 400\">Input Pipeline Optimizations<\/span><\/li>\n<li><span style=\"font-weight: 400\">Data Formats<\/span><\/li>\n<li><span style=\"font-weight: 400\">Common Fused Operations<\/span><\/li>\n<li><span style=\"font-weight: 400\">RNN Performance<\/span><\/li>\n<li><span style=\"font-weight: 400\">Building &amp; Installing from Source<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400\">a. Input Pipeline<\/span><\/h3>\n<p><span style=\"font-weight: 400\">The models that we build for our network get their input from the local disk drive and pre-processing is done on it before inputting it to the network. Consider, for example, a simple PNG image. <\/span><\/p>\n<p><span style=\"font-weight: 400\">The process flow that follows includes the loading of the image from the disk, converting it to a tensor followed by manipulating the tensor by cropping, padding and then making a batch. <\/span><\/p>\n<p><span style=\"font-weight: 400\">This is the process of Input Pipeline for TensorFlow Performance Optimization. The constriction occurs when the GPU\u2019s do a faster pre-processing.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Determining this bottleneck in the pipeline is a boring job. We can do this task by making a small model after the pipelining and then compare it with the full model, if the difference comes out to be minimal, there might be a constriction in the input pipeline. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Other ways including keeping a tab on the GPU for its usage and utilization.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">b. Data Formats <\/span><\/h3>\n<p><span style=\"font-weight: 400\">Data Formats is one of the ways for TensorFlow Performance Optimizations. As the name suggests, the structures of the input tensors that passes to the operations. Below are parameters of a 4D tensor:<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400\">N is the number of images in the batch.<\/span><\/li>\n<li><span style=\"font-weight: 400\">H is the number of pixels in vertical dimensions.<\/span><\/li>\n<li><span style=\"font-weight: 400\">W is the pixels in horizontal dimensions<\/span><span style=\"font-weight: 400\">.<\/span><\/li>\n<li><span style=\"font-weight: 400\">C is for channels.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">The nomenclature of these data formats in broadly split into two:<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400\">NCHW<\/span><\/li>\n<li><span style=\"font-weight: 400\">NHWC<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">The default is the latter whereas the firmer is the optimal choice while working on NVIDIA GPUs. It is good to build models that are compatible with both the formats so as to simplify the training on GPUs.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">c. Common Fused Ops<\/span><\/h3>\n<p><span style=\"font-weight: 400\">These operations merge more than one operation into a single kernel to improve the performance. XLA creates these operations whenever possible to improve performance automatically. Below few Ops can improve the TensorFlow performance.<\/span><\/p>\n<h4><strong>i. Fused Batch Normalization<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">An expensive process in TensorFlow Performance Optimization with a large amount of operation time. We use it to combine several operations into a single kernel to perform the batch normalization. Using this can speed up the process up to 12-30%. The two ways to perform batch norms are:<\/span><\/p>\n<p><span style=\"font-weight: 400\">The <\/span><b><i>tf.layers.batch_normailzation<\/i><\/b><span style=\"font-weight: 400\">.<\/span><br \/>\n<span style=\"font-weight: 400\">bn = tf.layers.batch_normalization(<\/span><span style=\"font-weight: 400\"><br \/>\n<\/span><span style=\"font-weight: 400\">\u00a0 \u00a0 input_layer, fused=<\/span><span style=\"font-weight: 400\">True<\/span><span style=\"font-weight: 400\">, data_format=<\/span><span style=\"font-weight: 400\">&#8216;NCHW&#8217;<\/span><span style=\"font-weight: 400\">)<\/span><br \/>\n<span style=\"font-weight: 400\">The <\/span><b><i>tf.contrib.layers.batch_norm<\/i><\/b><span style=\"font-weight: 400\">.<\/span><br \/>\n<span style=\"font-weight: 400\">bn = tf.contrib.layers.batch_norm(input_layer, fused=<\/span><span style=\"font-weight: 400\">True<\/span><span style=\"font-weight: 400\">, data_format=<\/span><span style=\"font-weight: 400\">&#8216;NCHW&#8217;<\/span><span style=\"font-weight: 400\">)<\/span><\/p>\n<h3><span style=\"font-weight: 400\">d. RNN Performance<\/span><\/h3>\n<p><span style=\"font-weight: 400\">We can specify the recurrent network in many ways. A reference that may be considered for implementation is the <\/span><b><i>tf.nn.rnn_cell.BasicLSTMCell.<\/i><\/b><span style=\"font-weight: 400\"> Now, you have a choice between <\/span><b><i>tf.nn.static_rnn<\/i><\/b><span style=\"font-weight: 400\"> and <\/span><b><i>tf.nn.dynamic_rnn <\/i><\/b><span style=\"font-weight: 400\">while running a cell. <\/span><\/p>\n<p><span style=\"font-weight: 400\">An advantage of <\/span><b><i>tf.nn.dynamic_rnn<\/i><\/b><span style=\"font-weight: 400\"> is that it can optionally swap memory from the GPU to the CPU which might be helpful in some with or you can get a degraded performance depending on the hardware. Multiple loops of tf.nn.dynamic_rnn and the tf.while_loop construct could be run in parallel.<\/span><\/p>\n<p><span style=\"font-weight: 400\">The <\/span><b><i>tf.contrib.cudnn_rnn<\/i><\/b><span style=\"font-weight: 400\"> should always be used in NVIDIA GPUs while in CPUs when <\/span><b><i>tf.contrib.cudnn_rnn<\/i><\/b><span style=\"font-weight: 400\"> is not available, the fastest alternative is the <\/span><b><i>tf.contrib.rnn.LSTMBlockFusedCell<\/i><\/b><span style=\"font-weight: 400\">.<\/span><\/p>\n<p><span style=\"font-weight: 400\">The other cell types which are less common, we can implement the graph like <\/span><b><i>tf.contrib.rnn.BasicLSTMCell<\/i><\/b><span style=\"font-weight: 400\"> and will have same characteristics including degraded performance and usage of high memory. <\/span><\/p>\n<h3><span style=\"font-weight: 400\">e. Building and Installing From Source<\/span><\/h3>\n<p><span style=\"font-weight: 400\">We should run Tensorflow with all the optimizations available in order to compile using CPU. Building and installing from source lets you install the optimized version of TensorFlow. Now if the host platform differs from the target, then cross-compiler has the highest optimization.<\/span><\/p>\n<h2><span style=\"font-weight: 400\">Optimizing for GPU<\/span><\/h2>\n<p><span style=\"font-weight: 400\">This section covers the <a href=\"https:\/\/www.tensorflow.org\/\">TensorFlow<\/a> Performance Optimization methods which not usually practised. Optimal performance with the GPU seems to be the ultimate aim and one of the ways of doing this is with the use of data parallelism. <\/span><\/p>\n<p><span style=\"font-weight: 400\">We can obtain parallelism by making multiple copies of the model that refers to as towers. The following points will help you to understand better: \u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Placing one tower on each of the GPUs. The towers work on different batch sizes of data, updating variables (parameters) which shares between each tower. <\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Updation of these parameters depends upon the model, the configuration of the hardware. Given is a corollary from benchmarking various platforms and configurations:<\/span><\/li>\n<\/ul>\n<ol>\n<li style=\"font-weight: 400\"><b>Tesla K80<\/b><span style=\"font-weight: 400\">: Here, the GPUs are on the same PCI express card and the peer to peer functionality of the NVIDIA is present, therefore, placing the variables equally across the GPUs that are used for training will give the best results. <\/span><\/li>\n<li style=\"font-weight: 400\"><b>Titan X<\/b><span style=\"font-weight: 400\"> (Maxwell and Pascal), <\/span><b>M 40<\/b><span style=\"font-weight: 400\">, <\/span><b>and P100<\/b><span style=\"font-weight: 400\">: For models like ResNet and InceptionV3, variables should be placed on the GPU for optimum performance, but for models with variables that are large in numbers like AlexNet and VGG, NCCL is a better choice.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">In general, a method is here\u00a0 to find where operations place to manage variables and that method replaces the device name when calling with tf.device()<\/span><\/li>\n<\/ol>\n<h2><span style=\"font-weight: 400\">Optimizing for CPU<\/span><\/h2>\n<p><span style=\"font-weight: 400\">There are two configurations below for optimizing CPU performance.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><b><i>intra_op_parallelism<\/i><\/b><span style=\"font-weight: 400\">: parallelization of nodes is achieved using multi-threads, that\u2019ll schedule the individual pieces.<\/span><\/li>\n<li style=\"font-weight: 400\"><b><i>inter_op_parallelism<\/i><\/b><span style=\"font-weight: 400\">: The nodes that are ready are scheduled in this operation.<\/span><\/li>\n<\/ul>\n<p><b><i>tf.ConfigProto<\/i><\/b><span style=\"font-weight: 400\">, is used to set these configurations by passing to in the config attribute of <\/span><b><i>tf.Session<\/i><\/b><span style=\"font-weight: 400\">. For both parallelism configurations, is initialised as zero, they will revert to the number of logical CPU cores. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Equating the number of physical cores to the number of threads rather than using logical cores is another good way of optimization.<\/span><\/p>\n<p>So, this was all about TensorFlow Performance Optimization. Hope you like and satisfied with our explanation of how to optimize TensorFlow Performance.<\/p>\n<h2><span style=\"font-weight: 400\">Conclusion &#8211; TensorFlow Performance Optimization<\/span><\/h2>\n<p><span style=\"font-weight: 400\">Hence, in this TensorFlow Performance Optimization tutorial, we saw, there are various ways of optimizing TensorFlow Performance of our computation, the main one being the up-gradation of hardware which often is costly. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Moreover, we saw Optimizing for GPU and Optimizing for CPU which also helps to improve TensorFlow Performance. There are other methods as you saw like data parallelism and multi-threading that will push the current hardware to their limit, giving you the best results that you can get out of them. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Next up, is the comparison between the two mobile platforms of TensorFlow. Furthermore, if you have any doubt regarding TensorFlow Performance Optimization, feel free to ask through the comment section.<\/span><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:1606,&quot;href&quot;:&quot;https:\\\/\\\/www.tensorflow.org&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251012130405\\\/https:\\\/\\\/www.tensorflow.org\\\/&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-09 13:03:33&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2025-12-15 00:56:14&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2025-12-18 07:39:14&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2025-12-22 11:40:17&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2025-12-25 19:36:25&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-01-06 23:35:49&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-01-10 11:44:59&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-01-15 21:00:07&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-01-19 12:11:54&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-23 14:26:32&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-01-27 04:57:21&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-02-01 13:27:13&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-02-08 07:39:35&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-02-14 11:01:58&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-02-18 12:08:03&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-03-01 02:18:33&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-03-04 14:33:45&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-03-09 01:19:37&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-03-27 08:51:43&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-04-01 12:29:08&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-04-14 05:35:56&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-04-18 08:59:53&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-04-23 09:03:59&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-05-03 09:43:08&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-05-06 22:37:12&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-05-14 17:26:18&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-05-19 04:42:33&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-05-22 06:23:42&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-05-30 12:14:16&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-06-05 00:52:09&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-06-15 12:28:28&quot;,&quot;http_code&quot;:503}],&quot;broken&quot;:true,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-15 12:28:28&quot;,&quot;http_code&quot;:503},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Today, in this TensorFlow Performance Optimization Tutorial, we\u2019ll be getting to know how to optimize the performance of our TensorFlow code. The article will help us to understand the need for optimization and the&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":16212,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[73],"tags":[9297,9300,9301,9528,14529,14536,14550,14591,14594,14595],"class_list":["post-16185","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tensorflow","tag-optimization-of-tensorflow","tag-optimizing-for-cpu","tag-optimizing-for-gpu","tag-pipeline-optimization","tag-tensorflow-code","tag-tensorflow-cpu-vs-gpu","tag-tensorflow-gpu","tag-tensorflow-optimizer","tag-tensorflow-performance","tag-tensorflow-performance-optimization"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>TensorFlow Performance Optimization - Tips To Improve Performance - DataFlair<\/title>\n<meta name=\"description\" content=\"TensorFlow Performance Optimization, ways for improving TensorFlow Performance, Optimizing for CPU, TensorFlow GPU, Pipeline Optimization, CPU vs GPU\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/tensorflow-performance-optimization\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"TensorFlow Performance Optimization - 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