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      "body": "<p style=\"text-align: center;\">Such compute. </p>\n<p style=\"text-align: right;\">very co$t</[>\n<p style=\"text-align: left;\">Wow</p>\n\nThe price of a pair of GTX 1060 GPUs has gone up about 50% since I <a href=\"https://steemit.com/deeplearning/@thescinder/i-too-built-a-rather-decent-deep-learning-rig-for-900-quid\">built my deep-learning rig</a> a few weeks ago, and that's if you can even find them in stock. There's been a *wee bit* of a gold rush surrounding cryptocurrencies lately as many new miners have been setting up systems. Ultimately, I think this will benefit more than just crypto as the demand for fast and efficient cards pushes graphics card makers to innovate more efficient and powerful cards, just like high performance computing for scientific purposes has traditionally piggy-backed on demand for better gaming. It also means increased awareness and adoption of cryptography and cryptocurrencies, which I consider a good thing as it should help stabilize the ecosystem. \n\nIn any case, whether your aim is mining ether or back-propagation, you may want to get as much performance out of the GPUs you do have during the current shortage. This means tuning the card to optimize for your needs of performance and/or efficiency. For Nvidia cards on Ubuntu this comes with a slight difficulty in that normally you can only tune a GPU running a display, but with a few tricks it's possible to overclock multiple GPUs without hooking up a monitor to each one. This took me a while to figure out, so I thought it may be helpful to others. \n\nNote: changing the <code>cool-bits</code> flag lets you bypass thermal safeguards, may affect warranty, etc., so be conservative in your changes and monitor for GPU temperatures and errors. I typically run the fans at a higher intensity than they would normally operate and keep the temperature well below 70C. \n\nIn short, it was the order that mattered. Setting <code>cool-bits</code> with or without a a flag to allow empty configurations, before editing the config file always left me with control over just one GPU after rebooting :-/ Instead I had to first modify the config file, and only then allow empty configurations and change the cool-bits flag. I'll assume you've got your drivers set up and your cards are working, all you have left is to gain control of <code>nvidia-settings.</code> Here's what worked for me:\n\nFirst edit your Xorg config file. Duplicate the monitor/device/screen declarations while incrementing the names, <em>e.g.</em> \"Device0\" becomes \"Device1.\"  Do this as many times as you need to for each of your GPUs, I have two cards so I ended up with two screens/monitors/device entries. The text for my config file is at the end of this post. \n<code>\nsudo nano /etc/X11/xorg.conf\n</code>\n<em>nano --> your text editor of choice</em>\n\nThen set the -cool-bits flag and allow empty configurations. Setting <code>cool-bits</code>  to 28 actually <a href=\"https://hashcat.net/forum/thread-5785-post-31020.html\">allows</a> you to change GPU voltages, which I don't currently use or recommend. 12 or 5 should also work for our needs. \n\n<code>\nsudo nvidia-xconfig -a --cool-bits=28 --allow-empty-initial-configuration\n</code>\n\nand that's it. You should be able to reboot and start over/underclocking your GPUs. Check in on the temperature and power usage with <code>nvidia-smi</code> on the command line. You can modify the thermal and performance (\"Powermizer\") settings with the GUI by just typing <code>nvidia-settings</code>, or you can use commands like these:\n\n<code>\nnvidia-settings -a [gpu:0]/GPUMemoryTransferRateOffset[3]=400\nnvidia-settings -a [gpu:0]/GPUGraphicsClockOffset[3]=40\nnvidia-settings -a [gpu:0]/GPUFanControlState=1\nnvidia-settings -a [fan:0]/GPUTargetFanSpeed=65\n</code>\n<em>or back to normal</em>\n<code>\nnvidia-settings -a [gpu:0]/GPUMemoryTransferRateOffset[3]=0\nnvidia-settings -a [gpu:0]/GPUGraphicsClockOffset[3]=0\nnvidia-settings -a [gpu:0]/GPUFanControlState=0\n</code>\nTune the settings in small increments and just change one setting at a time until you get closer to optimizing your chosen metric, then adjust the next setting and repeat as necessary (\"walking the settings\"). There are many more, much better guides out there for the actual overclocking for performance or underclocking for efficiency, and I suggest you check them out. Hopefully one or two other people had the same problem as I did with the order of setting cool-bits and modifying the xorg.conf file and this short post will be useful to some fellow human, somewhere, sometime. Thanks!\n\nI am using Ubuntu 16.04 with the 375.66 version Nvidia drivers. \n\nxorg.conf example:\n```\n# nvidia-xconfig: X configuration file generated by nvidia-xconfig\n# nvidia-xconfig:  version 375.66  (buildmeister@swio-display-x86-rhel47-06)  Mon May  1 15:45:32 PDT 2017\n\nSection \"ServerLayout\"\n    Identifier     \"Layout0\"\n    Screen      0  \"Screen0\"\n    Screen      1  \"Screen1\" RightOf \"Screen0\"\n    InputDevice    \"Keyboard0\" \"CoreKeyboard\"\n    InputDevice    \"Mouse0\" \"CorePointer\"\nEndSection\n\nSection \"Files\"\nEndSection\n\nSection \"InputDevice\"\n    # generated from default\n    Identifier     \"Mouse0\"\n    Driver         \"mouse\"\n    Option         \"Protocol\" \"auto\"\n    Option         \"Device\" \"/dev/psaux\"\n    Option         \"Emulate3Buttons\" \"no\"\n    Option         \"ZAxisMapping\" \"4 5\"\nEndSection\n\nSection \"InputDevice\"\n    # generated from default\n    Identifier     \"Keyboard0\"\n    Driver         \"kbd\"\nEndSection\n\nSection \"Monitor\"\n    Identifier     \"Monitor0\"\n    VendorName     \"Unknown\"\n    ModelName      \"Unknown\"\n    HorizSync       28.0 - 33.0\n    VertRefresh     43.0 - 72.0\n    Option         \"DPMS\"\nEndSection\n\nSection \"Monitor\"\n    Identifier     \"Monitor1\"\n    VendorName     \"Unknown\"\n    ModelName      \"Unknown\"\n    HorizSync       28.0 - 33.0\n    VertRefresh     43.0 - 72.0\n    Option         \"DPMS\"\nEndSection\n\nSection \"Device\"\n    Identifier     \"Device0\"\n    Driver         \"nvidia\"\n    VendorName     \"NVIDIA Corporation\"\n    BoardName      \"GeForce GTX 1060 6GB\"\n    BusID          \"PCI:1:0:0\"\nEndSection\n\nSection \"Device\"\n    Identifier     \"Device1\"\n    Driver         \"nvidia\"\n    VendorName     \"NVIDIA Corporation\"\n    BoardName      \"GeForce GTX 1060 6GB\"\n    BusID          \"PCI:2:0:0\"\nEndSection\n\nSection \"Screen\"\n    Identifier     \"Screen0\"\n    Device         \"Device0\"\n    Monitor        \"Monitor0\"\n    DefaultDepth    24\n    Option         \"AllowEmptyInitialConfiguration\" \"True\"\n    Option         \"Coolbits\" \"28\"\n    SubSection     \"Display\"\n        Depth       24\n    EndSubSection\nEndSection\n\nSection \"Screen\"\n    Identifier     \"Screen1\"\n    Device         \"Device1\"\n    Monitor        \"Monitor1\"\n    DefaultDepth    24\n    Option         \"AllowEmptyInitialConfiguration\" \"True\"\n    Option         \"Coolbits\" \"28\"\n    SubSection     \"Display\"\n        Depth       24\n    EndSubSection\nEndSection\n\n```",
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      "title": "I too built a rather decent deep learning rig for 900 quid",
      "body": "@@ -1,102 +1,4 @@\n-%3Ca href=%22#tldr%22%3ESkip to the components list%3C/a%3E%0A%3Ca href=%22#benchmarks%22%3ESkip to the benchmarks%3C/a%3E%0A%0A\n Robe\n@@ -12490,16 +12490,123 @@\n ask.  %0A%0A\n+%3Cem%3EEdit: removed the hyperlinks to the benchmarks and component list as they don't seem to be working%3C/a%3E%0A\n %3Cem%3EOrig\n",
      "json_metadata": "{\"tags\":[\"deeplearning\",\"gpu\",\"machinelearning\",\"nvidia\",\"tensorflow\"],\"image\":[\"https://thescinder.files.wordpress.com/2017/06/goingtoneedagpuimgflip1.jpg?w=840\",\"https://thescinder.files.wordpress.com/2017/06/cifar10training1.png\",\"https://thescinder.files.wordpress.com/2017/06/2x2cnntraining.png\",\"https://thescinder.files.wordpress.com/2017/06/singlegpucifar10.png\",\"https://thescinder.files.wordpress.com/2017/06/dualgpucifar10.png\"],\"links\":[\"https://en.wikipedia.org/wiki/Deep_Blue_versus_Garry_Kasparov\",\"https://en.wikipedia.org/wiki/AlphaGo\",\"https://thescinder.files.wordpress.com/2017/06/goingtoneedagpuimgflip1.jpg\",\"https://medium.com/towards-data-science/build-a-deep-learning-rig-for-800-4434e21a424f\",\"https://blog.slavv.com/the-1700-great-deep-learning-box-assembly-setup-and-benchmarks-148c5ebe6415\",\"https://www.analyticsvidhya.com/blog/2016/11/building-a-machine-learning-deep-learning-workstation-for-under-5000/\",\"http://timdettmers.com/2017/04/09/which-gpu-for-deep-learning/\",\"https://developer.nvidia.com/cuda-downloads\",\"https://www.tensorflow.org/install/install_linux#nvidia_requirements_to_run_tensorflow_with_gpu_support\",\"https://www.continuum.io/downloads\",\"https://www.tensorflow.org/tutorials/deep_cnn\",\"https://thescinder.files.wordpress.com/2017/06/cifar10training1.png\",\"https://thescinder.files.wordpress.com/2017/06/2x2cnntraining.png\",\"https://thescinder.files.wordpress.com/2017/06/singlegpucifar10.png\",\"https://thescinder.files.wordpress.com/2017/06/dualgpucifar10.png\",\"https://thescinder.com/2017/06/25/i-too-built-a-rather-decent-deep-learning-rig-for-900-quid/\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}"
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      "body": "Very interesting post. Great to see what is already possible from home.\n\nI am looking for somebody who can write code to analyze language into phonetics and then make the software find phonetically similar words. Can you do that?",
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      "body": "<a href=\"#tldr\">Skip to the components list</a>\n<a href=\"#benchmarks\">Skip to the benchmarks</a>\n\nRobert Heinlein's 1957 <em>Door into Summer</em> returns throughout\tto a theme of knowing when it's \"time to railroad.\" Loosely speaking this is the idea that one's success comes as much from historical context as it does from innate ability, hard work, and luck (though much of the latter can be attributed to historical context). \n\nMuch of the concepts driving our modern AI renaissance are decades old at least- but the field lost steam as the computers were too slow and the Amazookles of the world were yet to use them to power their recommendation engines and so on. In the meantime computers have gotten much faster and much <a href=\"https://en.wikipedia.org/wiki/Deep_Blue_versus_Garry_Kasparov\">better</a> at <a href=\"https://en.wikipedia.org/wiki/AlphaGo\">beating humans</a> at very fancy games. Modern computers are now fast enough to make deep learning feasible. Without being entirely scrutable (much of the learning happening unpredictably), DL works for many problems and as a bonus could provide insight into how our own minds might work. \n\nI too have seen the writing on the wall in recent years. I can say with some confidence that now is the time to railroad, and by \"railroad\" I mean revolutionise the world with artificial intelligence. A lot of things changed in big ways during the original \"time to railroad,\" the industrial revolution. For some this meant fortune and progress and for others, ruin. I would like to think that we are all a bit brighter than our old-timey counterparts were back then and we have the benefit of our history to learn from, so I'm rooting for an egalitarian utopia rather than an AI apocalypse. In any case, collective stewardship of the sea changes underway is important and this means the more people learn about AI the less likely the future will be decided solely by the technocratic elites of today.\n\nI've completed a few MOOCs on machine learning in general and neural networks in particular, coded up some of the basic functions from scratch and I'm beginning to use some of the major libraries to investigate more interesting ideas. As I moved on from toy examples like MNIST and housing price prediction one thing became increasingly clear:\n\n<a href=\"https://thescinder.files.wordpress.com/2017/06/goingtoneedagpuimgflip1.jpg\"><img src=\"https://thescinder.files.wordpress.com/2017/06/goingtoneedagpuimgflip1.jpg?w=840\" alt=\"\" width=\"840\" height=\"472\" class=\"aligncenter size-large wp-image-1699\" /></a>\n\n\nIt took me a week of work to realize I was totally on the wrong track training a vision model meant to mimic cuttlefish perception on my laptop. This sort of wasted time really adds up, so I decided to go deeper and build my own GPU-enhanced deep learning rig. \n\nLuckily there are lots of great guides out there as everyone and their grandmother is building their own DL rig at the moment. Most of the build guides have something along the lines of \". . . for xxxx monies\" in the title, which makes it easier to match budgets. Build budgets run the gamut from the surprisingly capable <a href=\"https://medium.com/towards-data-science/build-a-deep-learning-rig-for-800-4434e21a424f\">$800 machine</a> by Nick Condo to the serious <a href=\"https://blog.slavv.com/the-1700-great-deep-learning-box-assembly-setup-and-benchmarks-148c5ebe6415\">$1700 machine</a> by Slav Ivanov all the way up to the low low price of <a href=\"https://www.analyticsvidhya.com/blog/2016/11/building-a-machine-learning-deep-learning-workstation-for-under-5000/\">\"under $5000\"</a> by Kunal Jain. I did not even read the last one because I am not made of money. \n\nI am currently living in the UK, so that means I have to buy everything in pounds. The prices for components in pounds sterling are. . . pretty much the same as they are in greenbacks. The exchange rate to the British pound can be a bit misleading, even now that Brexit has crushed the pound sterling as well as our hopes and dreams. In my experience it seems like you can buy about the same for a pound at the store as for a dollar in the US or a euro on the continent. It seems like the only thing they use the exchange rate for is calculating salaries. \n\nI'd recommend first visiting Tim Dettmers' guide to <a href=\"http://timdettmers.com/2017/04/09/which-gpu-for-deep-learning/\">choosing the right GPU for you</a>. I'm in a stage of life where buying the \"second cheapest\" appropriate option is usually best. With a little additional background reading and following Tim's guide, I selected the Nvidia GTX 1060 GPU with 6GB of memory. This was from Tim's \"I have little money\" category, one up from the \"I have almost no money\" category, and in keeping with my life philosophy of the second-cheapest. Going to the next tier up is often close to  twice as costly, but not close to twice as good. This holds for my choice of GPUs as well: a single 1070 is about twice the cost and around 50% or so faster than a 1060 However, two 1060s does get you pretty close to twice the performance, and that's what I went with. As we'll see in the benchmarks Tensorflow does make it pretty easy to take advantage of both GPUs, but doubling the capacity of my DLR by doubling the GPUs in the future won't be plausible.  \n\nMy upgradeability is somewhat limited by the number of threads (4) and PCIe lanes (16) of the modest i3 CPU I chose; if a near-term upgrade was a higher priority, I should have left out the second 1060 GPU and diverted that part of a budget to a better CPU (e.g. the <a href=\"https://blog.slavv.com/the-1700-great-deep-learning-box-assembly-setup-and-benchmarks-148c5ebe6415\">Intel Xeon E5-1620 V4</a> recommended <a href=\"https://blog.slavv.com/the-1700-great-deep-learning-box-assembly-setup-and-benchmarks-148c5ebe6415\">by Slav Ivanov</a>). But if you're shelling out so much for a higher-end system you'll probably want a bigger GPU to start with, and it's easy to see how one can go from a budget of $800 to $1700 rather quickly. \n\nThe rest of the computer's job is to quickly dump data into the GPU memory without messing things up. I ended up using almost all the same components as those in <a href=\"https://medium.com/towards-data-science/build-a-deep-learning-rig-for-800-4434e21a424f\">Nick's guide</a> because, again, my physical makeup is meat rather than monetary in nature. \n\nHere's the full list of components. I sourced what I could from Amazon Warehouse Deals to try and keep the cost down. \n\n<a id=\"tldr\"></a>\n<b>GPU (x2)</b>: Gigabyte Nvidia GTX 1060 6GB (£205.78 each)\n<b>Motherboard</b>: MSI Intel Z170 KRAIT-GAMING (£99.95)\n<b>CPU</b>: Intel Core i3 6100 Skylake Dual-Core 3.7 GHz Processor (£94.58)\n<b>Memory</b>: Corsair CMK16GX4M2A2400C14 Vengeance 2x8GB (1£05.78)\n<b>PSU</b>: Corsair CP-9020078-UK Builder Series 750W CS750M ATX/EPS Semi-Modular 80 Plus Gold Power Supply Unit (£77.25)\n<b>Storage</b>: SanDisk Ultra II SSD 240 GB SATA III (£72.18)\n<b>Case</b>: Thermaltake Versa H23 (27.10)\n\n<b>Total: £888.40</b>\n\n\nI had never built a PC before and didn't have any idea what I was doing. Luckily, Youtube did, and I didn't even break anything when I slotted all the pieces together. I had an install thumb drive for Ubuntu 16.04 hanging around ready to go and consequently I was up and running relatively quickly. \n\nThe next step was installing the drivers and <a href=\"https://developer.nvidia.com/cuda-downloads\">CUDA developer's toolkit</a> for the GPUs. I've been working mainly with Tensorflow lately, so I followed <a href=\"https://www.tensorflow.org/install/install_linux#nvidia_requirements_to_run_tensorflow_with_gpu_support\">their guide</a> to get everything ready to take advantage of the new setup. I am using <a href=\"https://www.continuum.io/downloads\">Anaconda</a> to manage Python environments for now, so I made one with tensorflow and another with tensorflow_gpu packages. <b></b>\n\nI decided to train on the CIFAR10 image classification dataset using <a href=\"https://www.tensorflow.org/tutorials/deep_cnn\">this tutorial</a> to test out the GPUs. I also wanted to see how fast training progresses on a project of mine, a two-category classifier for quantitative phase microscope images. \n\n<a id=\"benchmarks\"></a>\n\nThe CIFAR10 image classification tutorial from tensorflow.org was perfect because you can flag for the training to take place on one or two GPUs, or train on the CPU alone. It takes ~1.25 hours to train the first 10000 steps on the CPU, but only 4 minutes for the same training on one 1060. That's a weeks-to-days/days-to-hours/hours-to-minutes level of speedup. \n<p align=\"center\">\n<a href=\"https://thescinder.files.wordpress.com/2017/06/cifar10training1.png\"><img src=\"https://thescinder.files.wordpress.com/2017/06/cifar10training1.png\" alt=\"\" width=\"555\" height=\"201\" class=\"aligncenter size-full wp-image-1709\" /></a>\n\n\n<a href=\"https://thescinder.files.wordpress.com/2017/06/2x2cnntraining.png\"><img src=\"https://thescinder.files.wordpress.com/2017/06/2x2cnntraining.png\" alt=\"\" width=\"529\" height=\"201\" class=\"aligncenter size-full wp-image-1697\" /></a>\n</p>\n\n# CPU 10000 steps\n2017-06-18 12:56:38.151978: step 0, loss = 4.68 (274.9 examples/sec; 0.466 sec/batch)\n2017-06-18 12:56:42.815268: step 10, loss = 4.60 (274.5 examples/sec; 0.466 sec/batch)\n...\n2017-06-18 14:12:50.121319: step 9980, loss = 0.80 (283.0 examples/sec; 0.452 sec/batch)\n2017-06-18 14:12:54.652866: step 9990, loss = 1.03 (282.5 examples/sec; 0.453 sec/batch)\n\n\n# One GPU\n2017-06-18 15:50:16.810051: step 0, loss = 4.67 (2.3 examples/sec; 56.496 sec/batch)\n2017-06-18 15:50:17.678610: step 10, loss = 4.62 (6139.0 examples/sec; 0.021 sec/batch)\n2017-06-18 15:50:17.886419: step 20, loss = 4.54 (6197.2 examples/sec; 0.021 sec/batch)\n...\n2017-06-18 15:54:00.386815: step 10000, loss = 0.96 (5823.0 examples/sec; 0.022 sec/batch)\n\n\n# Two GPUs\n2017-06-25 14:48:43.918359: step 0, loss = 4.68 (4.7 examples/sec; 27.362 sec/batch)\n2017-06-25 14:48:45.058762: step 10, loss = 4.61 (10065.4 examples/sec; 0.013 sec/batch)\n...\n2017-06-25 14:52:28.510590: step 6000, loss = 0.91 (8172.5 examples/sec; 0.016 sec/batch)\n...\n2017-06-25 14:54:56.087587: step 9990, loss = 0.90 (6167.8 examples/sec; 0.021 sec/batch)\n\nThat's about 21-32x speedup on the GPUs. Not quite double the speed on two GPUs because the model isn't big enough to utilize all of both GPUs, as we can see in the output from nvidia-smi \n\n# Training on one GPU\n<a href=\"https://thescinder.files.wordpress.com/2017/06/singlegpucifar10.png\"><img src=\"https://thescinder.files.wordpress.com/2017/06/singlegpucifar10.png\" alt=\"\" width=\"722\" height=\"212\" class=\"aligncenter size-full wp-image-1706\" /></a>\n\n# Training on two GPUs\n<a href=\"https://thescinder.files.wordpress.com/2017/06/dualgpucifar10.png\"><img src=\"https://thescinder.files.wordpress.com/2017/06/dualgpucifar10.png\" alt=\"\" width=\"731\" height=\"237\" class=\"aligncenter size-full wp-image-1707\" /></a>\n\nMy own model had a similar speedup, going from training about one 79-image minibatch per second to training more than 30 per second. Trying to train this model on my laptop, a Microsoft Surface Book, I was getting about 0.75 steps a second. [Aside: the laptop does have a discrete GPU, a variant of the GeForce 940M, but no Linux driver that I'm aware of :/].\n\n# Training on CPU only\nINFO:tensorflow:global_step/sec: 0.981465\nINFO:tensorflow:loss = 0.673449, step = 173 (101.889 sec)\nINFO:tensorflow:global_step/sec: 0.994314\nINFO:tensorflow:loss = 0.64968, step = 273 (100.572 sec)\n\n# Dual GPUs\nINFO:tensorflow:global_step/sec: 30.3432\nINFO:tensorflow:loss = 0.317435, step = 90801 (3.296 sec)\nINFO:tensorflow:global_step/sec: 30.6238\nINFO:tensorflow:loss = 0.272398, step = 90901 (3.265 sec)\nINFO:tensorflow:global_step/sec: 30.5632\nINFO:tensorflow:loss = 0.327474, step = 91001 (3.272 sec)\nINFO:tensorflow:global_step/sec: 30.5643\nINFO:tensorflow:loss = 0.43074, step = 91101 (3.272 sec)\nINFO:tensorflow:global_step/sec: 30.6085\n\nOverall I'm pretty satisfied with the setup, and I've got a lot of cool projects to try out on it. Getting the basics for machine learning is pretty easy with all the great MOOCs and tutorials out there, but the learning curve slopes sharply upward after that. Working directly on real projects with a machine that can train big models before the heat-death of the universe is essential for gaining intuition and tackling cool problems.\n\nAm I right to think that now is \"time to AI?\" I wanted to get an unbiased second opinion so now I am building the components of a synthetic mind to ask.  \n\n<em>Originally published on <a href=\"https://thescinder.com/2017/06/25/i-too-built-a-rather-decent-deep-learning-rig-for-900-quid/\">www.thescinder.com</a> by me. </em>",
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