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<div class="moz-cite-prefix">Hey Hugh,</div>
<div class="moz-cite-prefix"><br>
</div>
<div class="moz-cite-prefix">Thank you for your reply, see my
comments below.<br>
</div>
<div class="moz-cite-prefix"><br>
</div>
<div class="moz-cite-prefix">On 2019-07-22 9:30 a.m., D. Hugh
Redelmeier via talk wrote:<br>
</div>
<blockquote type="cite"
cite="mid:alpine.LFD.2.21.1907220855500.6898@redeye.mimosa.com">
<pre class="moz-quote-pre" wrap="">| From: Alex Volkov via talk <a class="moz-txt-link-rfc2396E" href="mailto:talk@gtalug.org"><talk@gtalug.org></a>
| I'm looking to buy used Nvidia GeForce GXT 1060 to run some ML
| tutorials.
The advantage of nvidia over AMD is wider support. CUDA is nvidia-only
(but AMD's ROCm is intended to be easy to port to from CUDA).</pre>
</blockquote>
<p>I have another system with Ryzen 5 2400G and was hoping to run
ROCm on it, but as it turns out -- ROCm doesn't fully support AMD
cards with built-in graphics. I still can install discreet card
into that system but the solution is not as cheap as getting a
used GTX off craigslist.</p>
<blockquote type="cite"
cite="mid:alpine.LFD.2.21.1907220855500.6898@redeye.mimosa.com">
<pre class="moz-quote-pre" wrap="">The disadvantage is that nvidia's stuff is closed source. Yuck.
nvidia also has terrible licensing terms that can force you to buy
more expensive cards. You probably won't be hit by this:
<a class="moz-txt-link-rfc2396E" href="https://www.theregister.co.uk/2018/01/03/nvidia_server_gpus/"><https://www.theregister.co.uk/2018/01/03/nvidia_server_gpus/></a></pre>
</blockquote>
<p>Yes. I haven't yet figured out how to fix screen tearing with
proprietary Nvidia drivers.<br>
</p>
<p>As for ML, I had to register on their website to download cuDNN
packages required for for tensorflow. Packages are for ubuntu, but
they seem to work on debian.</p>
<p>Nvidia asks some pretty invasive questions about how card is
going to be used (which I don't yet know), so randomly checking
off boxes and giving them one of the email addresses where I dump
all of the subscriptions helps.<br>
</p>
<blockquote type="cite"
cite="mid:alpine.LFD.2.21.1907220855500.6898@redeye.mimosa.com">
<pre class="moz-quote-pre" wrap="">In general, nvidia does more "price discrimination". But AMD is not
immune: AMD sells "workstation" cards for extra money.</pre>
</blockquote>
<p>I like AMD approach more, they just giving tensorflow-rocm binary
on pypi -- <a class="moz-txt-link-freetext" href="https://pypi.org/project/tensorflow-rocm/">https://pypi.org/project/tensorflow-rocm/</a></p>
<p>Nvidia, however, makes you jump through some hoops --
<a class="moz-txt-link-freetext" href="https://developer.nvidia.com/cudnn">https://developer.nvidia.com/cudnn</a><br>
</p>
<blockquote type="cite"
cite="mid:alpine.LFD.2.21.1907220855500.6898@redeye.mimosa.com">
<pre class="moz-quote-pre" wrap="">For raw computing power per dollar, my impression is that AMD can be a
better deal.</pre>
</blockquote>
<p>That seems to start to work around $500 price point, but I want
something to toy with, so I can't justify that expense right now.
Used GTX 1060 gives large enough performance to try things out, if
I grow out if it, I'll switch to cards that have good ROCm
support.<br>
</p>
<p>
</p>
<blockquote type="cite"
cite="mid:alpine.LFD.2.21.1907220855500.6898@redeye.mimosa.com">
<pre class="moz-quote-pre" wrap="">| I got a good deal on Dell OEM one. Are there any pitfalls in running one
| in a non-dell system? More specifically nothing even close to that i.e
| amd fx CPU and AMD 970 chipset.
There are no problems that I know of. I used a Dell OEM nvidia card
many years ago without issue.
Some OEM cards are a little crippled. My 5-year-old desktop came with
an OEM AMD card. The specs said "1920x1200 max resolution" but also
said "Dual Link DVI" (which is only needed for higher resolutions).
So I assumed that it could do 2560x1600 like the non-OEM versions. It
could not. (My best guess is that they cheaped-out on a TDMS chip and
did not, in fact, support dual-link, but I had no way to test.)
So check out the specs.</pre>
</blockquote>
<p>Turns out there are lot of $200 - $250 GTX 1060 cards being sold
on kijiji but the price for which they actually sell is much
lower. I offered $160 to 3 sellers, got reply from two, one was
the dell card which I was unsure about, the other was non-oem MSI
with dual fans. I went with MSI.</p>
<p>More fans more better.</p>
<blockquote type="cite"
cite="mid:alpine.LFD.2.21.1907220855500.6898@redeye.mimosa.com">
<pre class="moz-quote-pre" wrap="">Bonus hint: before buying the card, make sure it will fit in your
system:
- I had a problem with an RX 570 being too long for my computer's
motherboard
- many cards now require extra power connectors that your power supply
might not support. And the number of pins on those connectors
changed in recent years.
- you may need a power supply with more capacity.
- with more power comes more heat -- will your case handle that?
(Probably)</pre>
</blockquote>
<p>I'm really glad back in 2013 I bought decent mid-atx Antec
(Sonata II?) with 500W power supply with enough room inside and 2x
120mm fans. It has 2x 6-pin 12V connector. After minor AMDectomy
where I removed old Radeon that doesn't really do anything besides
displaying things, I was able to install and run the card without
any hardware changes to the rest of the system.</p>
<p>I got to the part when I'm able to run some tutorials on
tensorflow, which I do believe run on the CPU because there seem
to be a software bug somewhere related to LD_LIBRARY_PATH that I
still haven't figured out. <br>
</p>
<p><br>
</p>
<pre class="code highlight" lang="plaintext"><span id="LC81" class="line" lang="plaintext">2019-07-21 22:29:58.204355: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1</span>
<span id="LC82" class="line" lang="plaintext">2019-07-21 22:29:58.367642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: </span>
<span id="LC83" class="line" lang="plaintext">name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7715</span>
<span id="LC84" class="line" lang="plaintext">pciBusID: 0000:01:00.0</span>
<span id="LC85" class="line" lang="plaintext">2019-07-21 22:29:58.367858: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcudart.so.10.0'; dlerror: libcudart.so.10.0: cannot open shared object file: No such file or directory</span>
<span id="LC86" class="line" lang="plaintext">2019-07-21 22:29:58.367982: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcublas.so.10.0'; dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory</span>
<span id="LC87" class="line" lang="plaintext">2019-07-21 22:29:58.368112: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcufft.so.10.0'; dlerror: libcufft.so.10.0: cannot open shared object file: No such file or directory</span>
<span id="LC88" class="line" lang="plaintext">2019-07-21 22:29:58.368234: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcurand.so.10.0'; dlerror: libcurand.so.10.0: cannot open shared object file: No such file or directory</span>
<span id="LC89" class="line" lang="plaintext">2019-07-21 22:29:58.368369: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusolver.so.10.0'; dlerror: libcusolver.so.10.0: cannot open shared object file: No such file or directory</span>
<span id="LC90" class="line" lang="plaintext">2019-07-21 22:29:58.368498: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusparse.so.10.0'; dlerror: libcusparse.so.10.0: cannot open shared object file: No such file or directory</span>
<span id="LC91" class="line" lang="plaintext">2019-07-21 22:29:58.374333: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7</span>
<span id="LC92" class="line" lang="plaintext">2019-07-21 22:29:58.374376: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1663] Cannot dlopen some GPU libraries. Skipping registering GPU devices...</span>
<span id="LC93" class="line" lang="plaintext">2019-07-21 22:29:58.374999: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: FMA</span>
<span id="LC94" class="line" lang="plaintext">2019-07-21 22:29:58.405862: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3214800000 Hz</span>
<span id="LC95" class="line" lang="plaintext">2019-07-21 22:29:58.407424: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a967b9ab00 executing computations on platform Host. Devices:</span>
<span id="LC96" class="line" lang="plaintext">2019-07-21 22:29:58.407486: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined></span>
<span id="LC97" class="line" lang="plaintext">2019-07-21 22:29:58.563751: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a967b97fa0 executing computations on platform CUDA. Devices:</span>
<span id="LC98" class="line" lang="plaintext">2019-07-21 22:29:58.563835: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): GeForce GTX 1060 6GB, Compute Capability 6.1</span>
<span id="LC99" class="line" lang="plaintext">2019-07-21 22:29:58.564029: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:</span>
<span id="LC100" class="line" lang="plaintext">2019-07-21 22:29:58.564055: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187] </span>
<span id="LC101" class="line" lang="plaintext">2019-07-21 22:30:00.791745: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.</span>
<span id="LC102" class="line" lang="plaintext">W0721 22:30:01.203895 140325207855424 deprecation_wrapper.py:119] From classify_image.py:85: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.</span>
<span id="LC103" class="line" lang="plaintext"></span>
<span id="LC104" class="line" lang="plaintext">giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (score = 0.89107)</span>
<span id="LC105" class="line" lang="plaintext">indri, indris, Indri indri, Indri brevicaudatus (score = 0.00779)</span>
<span id="LC106" class="line" lang="plaintext">lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens (score = 0.00296)</span>
<span id="LC107" class="line" lang="plaintext">custard apple (score = 0.00147)</span>
<span id="LC108" class="line" lang="plaintext">earthstar (score = 0.00117)</span>
</pre>
<p><br>
</p>
Alex.<br>
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