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  • [MUSIC PLAYING]

  • TIM NOVIKOFF: Hi.

  • My name is Tim, and I'm the product manager for Colab.

  • For those of you who don't already know,

  • Colab allows you to write and execute arbitrary Python

  • code via the browser.

  • It's hosted Jupyter Notebooks by Google.

  • It's integrated with your Google account, which

  • means if you're signed into Chrome or signed into Gmail,

  • you're already signed into Colab.

  • And it's integrated with Google Drive, which

  • means that when you create a Jupyter Notebook in Colab,

  • it's automatically saved to your drive account.

  • Colab started in 2012 as an internal tool

  • for data analysts.

  • In 2014, we started using it for our internal machine learning

  • crash course.

  • This is actually where a lot of Googlers

  • first learned TensorFlow.

  • In 2017, we launched it publicly.

  • We wanted the whole world to have access

  • to the same great tool that Googlers were

  • using to get their work done.

  • Colab is easy to use.

  • When you connect with a virtual machine in a Colab notebook,

  • your Python environment is already set up for you.

  • The latest version of CUDA is already installed for you.

  • It just works.

  • So how do we do this?

  • Well, on the back end, we maintain

  • a pool of pre-warmed VMs.

  • These virtual machines have hundreds of popular Python

  • libraries pre-installed, including

  • TensorFlow, TensorBoard, TF privacy, TF data sets,

  • and many more.

  • There are resource limits in Colab.

  • And those are there to ensure sustainability and reduce

  • abuse.

  • Without any further ado, here are my top 10 Colab tricks

  • for TensorFlow users.

  • Number 10.

  • Always specify your TensorFlow version in Colab.

  • In Colab, when you import TensorFlow, by default today

  • it imports a version of TensorFlow 1.

  • But soon, it will by default import

  • a version of TensorFlow 2.

  • You can start using TensorFlow 2 in Colab

  • today by specifying your TensorFlow version

  • with the syntax shown on the screen.

  • If you have old Colab notebooks that use TensorFlow 1

  • and you want those to keep working well

  • after we switch to default TF 2, make

  • sure to specify TF 1.x in those notebooks.

  • That will futureproof those notebooks

  • so they'll keep working well.

  • The bottom line-- always specify your TensorFlow version

  • in Colab.

  • Number 9.

  • Use TensorBoard.

  • Colab output cells can render essentially arbitrary HTML.

  • And the TensorBoard team has taken advantage of this

  • to allow you to use TensorBoard right in line with Colab.

  • It works great, and I highly recommend it.

  • Number 8.

  • TFLite?

  • No problem.

  • Even though TFLite is for on-device machine learning,

  • you can still train your TFLite models in the cloud in Colab.

  • And in fact, that's a popular way to use TFLite.

  • Number 7.

  • Use TPUs.

  • It's free in Colab and very easy to do.

  • You just go to change runtime type

  • and select TPUs from the runtime--

  • from the dropdown.

  • If you want to feel the raw power of TPUs

  • under your fingertips, give them a whirl for free in Colab.

  • And with TensorFlow 2.1, TPUs have never been easier to use.

  • Number 6.

  • Use local runtimes if you want.

  • If you have your own workstation and your own GPUs perhaps

  • and you want to run your workload on your own hardware,

  • that's fine.

  • You can still actually use the Colab UI with a local runtime.

  • And that's easy to do as well.

  • Number 5.

  • The Colab scratchpad notebook.

  • If you find yourself with Colab notebooks that have names

  • like "untitled 17" and "untitled 18,"

  • the Cloud scratchpad notebook might be for you.

  • This is a special notebook available at the URL shown here

  • that's not automatically saved to your drive account.

  • And it's really great for doing your scratch work.

  • Number 4.

  • Copy your data into your Colab VM.

  • Rather than calling data on external storage or runtime

  • when you're, for example, training your models,

  • first copy all your data into the Colab VM.

  • This can result in speed-ups even if you're only

  • going to use the data once.

  • Number 3.

  • Mind your memory.

  • If you've ever run out of memory in Colab,

  • you may have noticed that perhaps later you were assigned

  • a special high RAM runtime.

  • Well, that may seem like a great thing.

  • But it also means that you're more

  • likely to run into the resource limits of Colab later.

  • So the best thing to do is to not run out of memory at all

  • and mind your memory when you're doing your work.

  • Number 2.

  • Close your tabs when you're done with Colab.

  • This will help you disconnect from the underlying virtual

  • machines sooner.

  • Again, conserving resources makes it less likely

  • that you'll run into the resource limits of Colab.

  • Finally, the number one trick, only use

  • GPUs when they're actually needed for your work.

  • Now, of course, when using TensorFlow,

  • often GPUs are needed for your work.

  • But when you're doing work that doesn't require a GPU,

  • just use a default runtime that only has a CPU.

  • Again, conserving resources helps

  • you get the most out of Colab by avoiding later

  • running into the resource limits of Colab.

  • Now, we do hear from users all the time things

  • like, "We want faster GPUs.

  • We want more reliable access to P100s.

  • We want longer runtimes.

  • We want more lenient idle time out periods.

  • And we want longer maximum VM lifetimes.

  • Or we want higher memory with more reliable access

  • to high RAM runtimes."

  • Well, we do hear you.

  • And that's why we recently launched Colab Pro.

  • Colab Pro comes with faster GPUs, longer runtimes, and more

  • memory for $10 a month.

  • We launched it in February, and it's been a big success.

  • It's only available in the United States for now,

  • but we're working as fast as we can

  • to bring it to more countries.

  • All right.

  • So what's next in Colab?

  • Well, first of all, the free version of Colab

  • is not going away.

  • The free version of Colab and Colab Pro

  • will continue to co-exist, and we're

  • continuing to work on improving both of them.

  • And as for feature development, well, that's

  • where I would like to hear from you.

  • Tweet at me.

  • Send feedback via the product.

  • Tweet at GoogleColab.

  • However you do it, let us know what

  • you want us to build next in Colab,

  • because it's user feedback from people like you

  • that drives our roadmap going forward.

  • So please let us know what you want next in Colab.

  • [MUSIC PLAYING]

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B1 中級

Colabを最大限に活用する (TF Dev Summit '20) (Making the most of Colab (TF Dev Summit '20))

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    林宜悉 に公開 2021 年 01 月 14 日
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