blocks|key|1523705|text|当GPU可用时，您需要稍后再试一次。该消息表明所有可用的GPU都在使用中。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1523706|常见问题提供了其他信息：|offset|length|1523707|我如何使用GPU，为什么它们有时不可用？
越野车是为了互动使用。可能会停止长期运行的后台计算，特别是在GPU上。请不要在加密货币挖掘中使用Colaboratory。这样做是不受支持的，并可能导致服务不可用。我们鼓励那些希望使用本地运行时运行连续或长时间运行的计算的用户。|blockquote|style|BOLD|1523708|使用GPU的持续训练似乎有一个冷却时间。因此，如果您遇到错误对话框，稍后再试一次，并可能尝试限制长期培训在以后的会议。|1523709|entityMap|0|LINK|mutability|MUTABLE|url|http://research.google.com/colaboratory/faq.html|1|http://research.google.com/colaboratory/local-runtimes.html^0|0|0|4|0|0|0|K|35|5|1|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|X|8|@]|9|@$D|Y|E|Z|1|10]]|A|$]]|$1|F|3|G|5|H|7|11|8|@$D|12|E|13|I|J]]|9|@$D|14|E|15|1|16]]|A|$]]|$1|K|3|L|5|6|7|17|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|18|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|T]]|U|$5|P|Q|R|A|$S|V]]]]

You'll need to try again later when a GPU is available. The message indicates that all available GPUs are in use.

The <a href="http://research.google.com/colaboratory/faq.html" rel="noreferrer">FAQ</a> provides additional info:

<blockquote>
 How may I use GPUs and why are they sometimes unavailable?
 
 Colaboratory is intended for interactive use. Long-running background
 computations, particularly on GPUs, may be stopped. Please do not use
 Colaboratory for cryptocurrency mining. Doing so is unsupported and
 may result in service unavailability. We encourage users who wish to
 run continuous or long-running computations through Colaboratory’s UI
 to use a <a href="http://research.google.com/colaboratory/local-runtimes.html" rel="noreferrer">local runtime</a>.
</blockquote>

There seems to be a cooldown on continuous training with GPUs. So, if you encounter the error dialog, try again later, and perhaps try to limit long-term training in subsequent sessions.

blocks|key|252380|text|您需要用GPU设备配置笔记本。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|252381|单击编辑->笔记本设置->硬件加速器->GPU|252382|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

You need to configure the Notebook with GPU device

Click Edit->notebook settings->hardware accelerator->GPU

blocks|key|1523837|text|加上一些图片，让它更清晰|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1523838|​|1523839|📷|atomic|offset|length|1523840|1523841|1523842|1523843|1523844|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/9aca6134b67a0d362b8772e7a9022151.png|imageAlt|1|https://developer.qcloudimg.com/http-save/yehe-900000/b3e188f6fdf4b611cabc74d70c74ad2c.png^0|0|0|0|1|0|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Y|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|Z|8|@]|9|@$G|10|H|11|1|12]]|A|$]]|$1|I|3|C|5|6|7|13|8|@]|9|@]|A|$]]|$1|J|3|C|5|6|7|14|8|@]|9|@]|A|$]]|$1|K|3|E|5|F|7|15|8|@]|9|@$G|16|H|17|1|18]]|A|$]]|$1|L|3|C|5|6|7|19|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|1A|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|T|U|-4]]|V|$5|P|Q|R|A|$S|W|U|-4]]]]

Add some pictures to make it clearer

<a href="https://i.stack.imgur.com/K5mVb.png" rel="noreferrer"><img src="https://i.stack.imgur.com/K5mVb.png" alt="enter image description here"></a>

<a href="https://i.stack.imgur.com/gSBYJ.png" rel="noreferrer"><img src="https://i.stack.imgur.com/gSBYJ.png" alt="enter image description here"></a>

blocks|key|252497|text|我的声誉有点太低，不能发表评论，但这里有一些额外的信息@鲍勃史密斯的答案，重新冷却期。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|252498|使用GPU的持续训练似乎有一个冷却时间。因此，如果您遇到错误对话框，稍后再试一次，并可能尝试限制长期培训在以后的会议。|blockquote|252499|根据我自己最近的经验，我相信Colab将分配给您最多12个小时的GPU使用，之后大约有8个小时的冷下来时间，然后您才能再次使用计算资源。在我的例子中，即使没有GPU，我也无法连接到实例。我不完全确定这下一步，但我想如果你一次运行3个实例，你的12个小时的消耗速度是原来的3倍。我不知道12小时的限值是在哪一段时间后重新设定的，但我猜可能有一天。|252500|无论如何，仍然缺少一些细节，但主要的注意是，如果你超过了你的限制，你将被锁在8小时内无法连接到一个实例(如果你正在积极地做一些事情，这将是一个很大的痛苦)。|252501|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$]]|$1|E|3|F|5|6|7|M|8|@]|9|@]|A|$]]|$1|G|3|H|5|6|7|N|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|O|8|@]|9|@]|A|$]]]|J|$]]

My reputation is just slightly too low to comment, but here's a bit of additional info for @Bob Smith's answer re cooldown period.

<blockquote>
 There seems to be a cooldown on continuous training with GPUs. So, if you encounter the error dialog, try again later, and perhaps try to limit long-term training in subsequent sessions.
</blockquote>

Based on my own recent experience, I believe Colab will allocate you at most 12 hours of GPU usage, after which there is roughly an 8 hour cool-down period before you can use compute resources again. In my case, I could not connect to an instance even without a GPU. I'm not entirely sure about this next bit but I think if you run say 3 instances at once, your 12 hours are depleted 3 times as fast. I don't know after what period of time the 12 hour limit resets, but I'd guess maybe a day. 

Anyway, still missing a few details but the main takeaway is that if you exceed you'll limit, you'll be locked out from connecting to an instance for 8 hours (which is a great pain if you're actively working on something).

blocks|key|1523763|text|在Reset+runtime没有工作之后，我做了：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1523764|Runtime+->+Reset+all+runtimes+->+Yes|1523765|然后我得到一个快乐：|1523766|在:+/device:GPU:0找到GPU|blockquote|1523767|entityMap^0|1|D|0|0|7|B|I|X|3|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|R|8|@$9|S|A|T|B|C]|$9|U|A|V|B|C]|$9|W|A|X|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|Y|8|@]|D|@]|E|$]]|$1|J|3|K|5|L|7|Z|8|@]|D|@]|E|$]]|$1|M|3|-4|5|6|7|10|8|@]|D|@]|E|$]]]|N|$]]

After <code>Reset runtime</code> didn't work, I did:

<code>Runtime</code> -> <code>Reset all runtimes</code> -> <code>Yes</code>

I then got a happy:

<blockquote>
<pre><code>Found GPU at: /device:GPU:0
</code></pre>
</blockquote>

blocks|key|252595|text|这是你问题的准确答案，伙计。根据Colab的一篇文章：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|252596|总体使用限制以及空闲超时时间、最大VM生存期、可用GPU类型以及其他因素随着时间的不同而不同。
GPU和TPU有时被优先用于交互使用Colab的用户，而不是长时间运行的计算，或者是最近在Colab中使用较少资源的用户。因此，使用Colab进行长时间计算的用户，或者最近在Colab中使用了更多资源的用户，更有可能遇到使用限制，并且对GPU和TPUs的访问权限受到暂时限制。具有高计算需求的用户可能对在自己的硬件上运行本地运行时使用Colab的UI感兴趣。|blockquote|252597|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|H|8|@]|9|@]|A|$]]|$1|E|3|-4|5|6|7|I|8|@]|9|@]|A|$]]]|F|$]]

This is the precise answer to your question man.
According to a post from Colab :
<blockquote>
overall usage limits, as well as idle timeout periods, maximum VM
lifetime, GPU types available, and other factors, vary over time.
GPUs and TPUs are sometimes prioritized for users who use Colab
interactively rather than for long-running computations, or for users
who have recently used less resources in Colab. As a result, users who
use Colab for long-running computations, or users who have recently
used more resources in Colab, are more likely to run into usage limits
and have their access to GPUs and TPUs temporarily restricted. Users
with high computational needs may be interested in using Colab’s UI
with a local runtime running on their own hardware.
</blockquote>

blocks|key|252537|text|默认情况下，Google+Colab有tensorflow+2.0，将其更改为tensorflow+1。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|252538|%25tensorflow_version+1.x在任何keras或tensorflow代码之前使用它。|offset|length|style|CODE|252539|entityMap^0|0|0|N|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@$D|L|E|M|F|G]]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

Google Colab has by default tensorflow 2.0, Change it to tensorflow 1. Add the code, 

<code>%tensorflow_version 1.x</code>
Use it before any keras or tensorflow code.

<a href="https://colab.research.google.com/notebook#fileId=/v2/external/notebooks/gpu.ipynb&amp;scrollTo=3IEVK-KFxi5Z" rel="noreferrer">Here</a> it is described how to use gpu with google-colaboratory:
<blockquote>
Simply select &quot;GPU&quot; in the Accelerator drop-down in Notebook Settings (either through the Edit menu or the command palette at cmd/ctrl-shift-P).
</blockquote>
However, when I select gpu in Notebook Settings I get a popup saying:
<blockquote>
Failed to assign a backend
No backend with GPU available. Would you like to use a runtime with no accelerator?
</blockquote>
When I run:
<pre class="lang-py prettyprint-override"><code>import tensorflow as tf
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
 raise SystemError('GPU device not found')
print('Found GPU at: {}'.format(device_name))
</code></pre>
Of course, I get <code>GPU device not found</code>. It seems the description is incomplete. Any ideas what needs to be done?

Google-colaboratory: No backend with GPU available

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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描述了如何将gpu与google-colaboratory一起使用：只需在笔记本设置中的加速器下拉菜单中选择"GPU“(通过编辑菜单或cmd/ctrl P处的命令调色板)。但是，当我在笔记本设置中选择gpu时，会看到一个弹出消息：未能为GPU可用的后端分配无后端。您想使用没有加速器的运行时吗？当我跑步时：import tensorflow as tfdevice_name = tf.test.gp

问Google-colaboratory:没有GPU的后端可用。
EN

回答 7

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问Google-colaboratory:没有GPU的后端可用。EN

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问Google-colaboratory:没有GPU的后端可用。
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