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@ -1,3 +1,5 @@
|
||||
Translating by vk
|
||||
|
||||
How to make a career move from proprietary to open source technology
|
||||
======
|
||||
|
||||
|
||||
@ -0,0 +1,75 @@
|
||||
Keeping (financial) score with Ledger –
|
||||
======
|
||||
I’ve used [Ledger CLI][1] to keep track of my finances since 2005, when I moved to Canada. I like the plain-text approach, and its support for virtual envelopes means that I can reconcile both my bank account balances and my virtual allocations to different categories. Here’s how we use those virtual envelopes to manage our finances separately.
|
||||
|
||||
Every month, I have an entry that moves things from my buffer of living expenses to various categories, including an allocation for household expenses. W- doesn’t ask for a lot, so I take care to be frugal with the difference between that and the cost of, say, living on my own. The way we handle it is that I cover a fixed amount, and this is credited by whatever I pay for groceries. Since our grocery total is usually less than the amount I budget for household expenses, any difference just stays on the tab. I used to write him cheques to even it out, but lately I just pay for the occasional additional large expense.
|
||||
|
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Here’s a sample envelope allocation:
|
||||
```
|
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2014.10.01 * Budget
|
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[Envelopes:Living]
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[Envelopes:Household] $500
|
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;; More lines go here
|
||||
|
||||
```
|
||||
|
||||
Here’s one of the envelope rules set up. This one encourages me to classify expenses properly. All expenses are taken out of my “Play” envelope.
|
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```
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= /^Expenses/
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(Envelopes:Play) -1.0
|
||||
|
||||
```
|
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|
||||
This one reimburses the “Play” envelope for household expenses, moving the amount from the “Household” envelope into the “Play” one.
|
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```
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= /^Expenses:House$/
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||||
(Envelopes:Play) 1.0
|
||||
(Envelopes:Household) -1.0
|
||||
|
||||
```
|
||||
|
||||
I have a regular set of expenses that simulate the household expenses coming out of my budget. For example, here’s the one for October.
|
||||
```
|
||||
2014.10.1 * House
|
||||
Expenses:House
|
||||
Assets:Household $-500
|
||||
|
||||
```
|
||||
|
||||
And this is what a grocery transaction looks like:
|
||||
```
|
||||
2014.09.28 * No Frills
|
||||
Assets:Household:Groceries $70.45
|
||||
Liabilities:MBNA:September $-70.45
|
||||
|
||||
```
|
||||
|
||||
Then `ledger bal Assets:Household` will tell me if I owe him money (negative balance) or not. If I pay for something large (ex: plane tickets, plumbing), the regular household expense budget gradually reduces that balance.
|
||||
|
||||
I picked up the trick of adding a month label to my credit card transactions from W-, who also uses Ledger to track his transactions. It lets me doublecheck the balance of a statement and see if the previous statement has been properly cleared.
|
||||
|
||||
It’s a bit of a weird use of the assets category, but it works out for me mentally.
|
||||
|
||||
Using Ledger to track it in this way lets me keep track of our grocery expenses and the difference between what I’ve actually paid and what I’ve budgeted for. If I end up spending more than I expected, I can move virtual money from more discretionary envelopes, so my budget always stays balanced.
|
||||
|
||||
Ledger’s a powerful tool. Pretty geeky, but maybe more descriptions of workflow might help people who are figuring things out!
|
||||
|
||||
More posts about: [finance][2] Tags: [ledger][3] | [See in index][4] // **[5 Comments »][5]**
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: http://sachachua.com/blog/2014/11/keeping-financial-score-ledger/
|
||||
|
||||
作者：[Sacha Chua][a]
|
||||
选题：[lujun9972](https://github.com/lujun9972)
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]:http://sachachua.com
|
||||
[1]:http://www.ledger-cli.org/
|
||||
[2]:http://sachachua.com/blog/category/finance/
|
||||
[3]:http://sachachua.com/blog/tag/ledger/
|
||||
[4]:http://pages.sachachua.com/sharing/blog.html?url=http://sachachua.com/blog/2014/11/keeping-financial-score-ledger/
|
||||
[5]:http://sachachua.com/blog/2014/11/keeping-financial-score-ledger/#comments
|
||||
@ -1,56 +0,0 @@
|
||||
Translating by qhwdw
|
||||
6 Open Source AI Tools to Know
|
||||
======
|
||||
|
||||
|
||||
|
||||
In open source, no matter how original your own idea seems, it is always wise to see if someone else has already executed the concept. For organizations and individuals interested in leveraging the growing power of artificial intelligence (AI), many of the best tools are not only free and open source, but, in many cases, have already been hardened and tested.
|
||||
|
||||
At leading companies and non-profit organizations, AI is a huge priority, and many of these companies and organizations are open sourcing valuable tools. Here is a sampling of free, open source AI tools available to anyone.
|
||||
|
||||
**Acumos.** [Acumos AI][1] is a platform and open source framework that makes it easy to build, share, and deploy AI apps. It standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment. This frees data scientists and model trainers to focus on their core competencies rather than endlessly customizing, modeling, and training an AI implementation.
|
||||
|
||||
Acumos is part of the[LF Deep Learning Foundation][2], an organization within The Linux Foundation that supports open source innovation in artificial intelligence, machine learning, and deep learning. The goal is to make these critical new technologies available to developers and data scientists, including those who may have limited experience with deep learning and AI. The LF Deep Learning Foundation just [recently approved a project lifecycle and contribution process][3] and is now accepting proposals for the contribution of projects.
|
||||
|
||||
**Facebook’s Framework.** Facebook[has open sourced][4] its central machine learning system designed for artificial intelligence tasks at large scale, and a series of other AI technologies. The tools are part of a proven platform in use at the company. Facebook has also open sourced a framework for deep learning and AI [called Caffe2][5].
|
||||
|
||||
**Speaking of Caffe.** Yahoo also released its key AI software under an open source license. The[CaffeOnSpark tool][6] is based on deep learning, a branch of artificial intelligence particularly useful in helping machines recognize human speech or the contents of a photo or video. Similarly, IBM’s machine learning program known as [SystemML][7] is freely available to share and modify through the Apache Software Foundation.
|
||||
|
||||
**Google’s Tools.** Google spent years developing its [TensorFlow][8] software framework to support its AI software and other predictive and analytics programs. TensorFlow is the engine behind several Google tools you may already use, including Google Photos and the speech recognition found in the Google app.
|
||||
|
||||
Two [AIY kits][9] open sourced by Google let individuals easily get hands-on with artificial intelligence. Focused on computer vision and voice assistants, the two kits come as small self-assembly cardboard boxes with all the components needed for use. The kits are currently available at Target in the United States, and are based on the open source Raspberry Pi platform — more evidence of how much is happening at the intersection of open source and AI.
|
||||
|
||||
**H2O.ai.** **** I[previously covered][10] H2O.ai, which has carved out a niche in the machine learning and artificial intelligence arena because its primary tools are free and open source. You can get the main H2O platform and Sparkling Water, which works with Apache Spark, simply by[downloading][11] them. These tools operate under the Apache 2.0 license, one of the most flexible open source licenses available, and you can even run them on clusters powered by Amazon Web Services (AWS) and others for just a few hundred dollars.
|
||||
|
||||
**Microsoft Onboard.** “Our goal is to democratize AI to empower every person and every organization to achieve more,” Microsoft CEO Satya Nadella[has said][12]. With that in mind, Microsoft is continuing to iterate its[Microsoft Cognitive Toolkit][13]. It’s an open source software framework that competes with tools such as TensorFlow and Caffe. Cognitive Toolkit works with both Windows and Linux on 64-bit platforms.
|
||||
|
||||
“Cognitive Toolkit enables enterprise-ready, production-grade AI by allowing users to create, train, and evaluate their own neural networks that can then scale efficiently across multiple GPUs and multiple machines on massive data sets,” reports the Cognitive Toolkit Team.
|
||||
|
||||
Learn more about AI in this new ebook from The Linux Foundation. [Open Source AI: Projects, Insights, and Trends by Ibrahim Haddad][14] surveys 16 popular open source AI projects – looking in depth at their histories, codebases, and GitHub contributions. [Download the free ebook now.][14]
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://www.linux.com/blog/2018/6/6-open-source-ai-tools-know
|
||||
|
||||
作者：[Sam Dean][a]
|
||||
选题：[lujun9972](https://github.com/lujun9972)
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]:https://www.linux.com/users/sam-dean
|
||||
[1]:https://www.acumos.org/
|
||||
[2]:https://www.linuxfoundation.org/projects/deep-learning/
|
||||
[3]:https://www.linuxfoundation.org/blog/lf-deep-learning-foundation-announces-project-contribution-process/
|
||||
[4]:https://code.facebook.com/posts/1687861518126048/facebook-to-open-source-ai-hardware-design/
|
||||
[5]:https://venturebeat.com/2017/04/18/facebook-open-sources-caffe2-a-new-deep-learning-framework/
|
||||
[6]:http://yahoohadoop.tumblr.com/post/139916563586/caffeonspark-open-sourced-for-distributed-deep
|
||||
[7]:https://systemml.apache.org/
|
||||
[8]:https://www.tensorflow.org/
|
||||
[9]:https://www.techradar.com/news/google-assistant-sweetens-raspberry-pi-with-ai-voice-control
|
||||
[10]:https://www.linux.com/news/sparkling-water-bridging-open-source-machine-learning-and-apache-spark
|
||||
[11]:http://www.h2o.ai/download
|
||||
[12]:https://blogs.msdn.microsoft.com/uk_faculty_connection/2017/02/10/microsoft-cognitive-toolkit-cntk/
|
||||
[13]:https://www.microsoft.com/en-us/cognitive-toolkit/
|
||||
[14]:https://www.linuxfoundation.org/publications/open-source-ai-projects-insights-and-trends/
|
||||
@ -1,67 +0,0 @@
|
||||
Translating by qhwdw
|
||||
Mesos and Kubernetes: It's Not a Competition
|
||||
======
|
||||
|
||||
|
||||
|
||||
The roots of Mesos can be traced back to 2009 when Ben Hindman was a PhD student at the University of California, Berkeley working on parallel programming. They were doing massive parallel computations on 128-core chips, trying to solve multiple problems such as making software and libraries run more efficiently on those chips. He started talking with fellow students so see if they could borrow ideas from parallel processing and multiple threads and apply them to cluster management.
|
||||
|
||||
“Initially, our focus was on Big Data,” said Hindman. Back then, Big Data was really hot and Hadoop was one of the hottest technologies. “We recognized that the way people were running things like Hadoop on clusters was similar to the way that people were running multiple threaded applications and parallel applications,” said Hindman.
|
||||
|
||||
However, it was not very efficient, so they started thinking how it could be done better through cluster management and resource management. “We looked at many different technologies at that time,” Hindman recalled.
|
||||
|
||||
Hindman and his colleagues, however, decided to adopt a novel approach. “We decided to create a lower level of abstraction for resource management, and run other services on top to that to do scheduling and other things,” said Hindman, “That’s essentially the essence of Mesos -- to separate out the resource management part from the scheduling part.”
|
||||
|
||||
It worked, and Mesos has been going strong ever since.
|
||||
|
||||
### The project goes to Apache
|
||||
|
||||
The project was founded in 2009. In 2010 the team decided to donate the project to the Apache Software Foundation (ASF). It was incubated at Apache and in 2013, it became a Top-Level Project (TLP).
|
||||
|
||||
There were many reasons why the Mesos community chose Apache Software Foundation, such as the permissiveness of Apache licensing, and the fact that they already had a vibrant community of other such projects.
|
||||
|
||||
It was also about influence. A lot of people working on Mesos were also involved with Apache, and many people were working on projects like Hadoop. At the same time, many folks from the Mesos community were working on other Big Data projects like Spark. This cross-pollination led all three projects -- Hadoop, Mesos, and Spark -- to become ASF projects.
|
||||
|
||||
It was also about commerce. Many companies were interested in Mesos, and the developers wanted it to be maintained by a neutral body instead of being a privately owned project.
|
||||
|
||||
### Who is using Mesos?
|
||||
|
||||
A better question would be, who isn’t? Everyone from Apple to Netflix is using Mesos. However, Mesos had its share of challenges that any technology faces in its early days. “Initially, I had to convince people that there was this new technology called ‘containers’ that could be interesting as there is no need to use virtual machines,” said Hindman.
|
||||
|
||||
The industry has changed a great deal since then, and now every conversation around infrastructure starts with ‘containers’ -- thanks to the work done by Docker. Today convincing is not needed, but even in the early days of Mesos, companies like Apple, Netflix, and PayPal saw the potential. They knew they could take advantage of containerization technologies in lieu of virtual machines. “These companies understood the value of containers before it became a phenomenon,” said Hindman.
|
||||
|
||||
These companies saw that they could have a bunch of containers, instead of virtual machines. All they needed was something to manage and run these containers, and they embraced Mesos. Some of the early users of Mesos included Apple, Netflix, PayPal, Yelp, OpenTable, and Groupon.
|
||||
|
||||
“Most of these organizations are using Mesos for just running arbitrary services,” said Hindman, “But there are many that are using it for doing interesting things with data processing, streaming data, analytics workloads and applications.”
|
||||
|
||||
One of the reasons these companies adopted Mesos was the clear separation between the resource management layers. Mesos offers the flexibility that companies need when dealing with containerization.
|
||||
|
||||
“One of the things we tried to do with Mesos was to create a layering so that people could take advantage of our layer, but also build whatever they wanted to on top,” said Hindman. “I think that's worked really well for the big organizations like Netflix and Apple.”
|
||||
|
||||
However, not every company is a tech company; not every company has or should have this expertise. To help those organizations, Hindman co-founded Mesosphere to offer services and solutions around Mesos. “We ultimately decided to build DC/OS for those organizations which didn’t have the technical expertise or didn't want to spend their time building something like that on top.”
|
||||
|
||||
### Mesos vs. Kubernetes?
|
||||
|
||||
People often think in terms of x versus y, but it’s not always a question of one technology versus another. Most technologies overlap in some areas, and they can also be complementary. “I don't tend to see all these things as competition. I think some of them actually can work in complementary ways with one another,” said Hindman.
|
||||
|
||||
“In fact the name Mesos stands for ‘middle’; it’s kind of a middle OS,” said Hindman, “We have the notion of a container scheduler that can be run on top of something like Mesos. When Kubernetes first came out, we actually embraced it in the Mesos ecosystem and saw it as another way of running containers in DC/OS on top of Mesos.”
|
||||
|
||||
Mesos also resurrected a project called [Marathon][1](a container orchestrator for Mesos and DC/OS), which they have made a first-class citizen in the Mesos ecosystem. However, Marathon does not really compare with Kubernetes. “Kubernetes does a lot more than what Marathon does, so you can’t swap them with each other,” said Hindman, “At the same time, we have done many things in Mesos that are not in Kubernetes. So, these technologies are complementary to each other.”
|
||||
|
||||
Instead of viewing such technologies as adversarial, they should be seen as beneficial to the industry. It’s not duplication of technologies; it’s diversity. According to Hindman, “it could be confusing for the end user in the open source space because it's hard to know which technologies are suitable for what kind of workload, but that’s the nature of the beast called Open Source.”
|
||||
|
||||
That just means there are more choices, and everybody wins.
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://www.linux.com/blog/2018/6/mesos-and-kubernetes-its-not-competition
|
||||
|
||||
作者：[Swapnil Bhartiya][a]
|
||||
选题：[lujun9972](https://github.com/lujun9972)
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]:https://www.linux.com/users/arnieswap
|
||||
[1]:https://mesosphere.github.io/marathon/
|
||||
@ -0,0 +1,143 @@
|
||||
Using Ledger for YNAB-like envelope budgeting
|
||||
======
|
||||
### Bye bye Elbank
|
||||
|
||||
I have to start this post with this: I will not be actively maintaining [Elbank][1] anymore, simply because I switched back to [Ledger][2]. If someone wants to take over, please contact me!
|
||||
|
||||
The main reason for switching is budgeting. While Elbank was a cool experiment, it is not an accounting software, and inherently lacks support for powerful budgeting.
|
||||
|
||||
When I started working on Elbank as a replacement for Ledger, I was looking for a reporting tool within Emacs that would fetch bank transactions automatically, so I wouldn’t have to enter transactions by hand (this is a seriously tedious task, and I grew tired of doing it after roughly two years, and finally gave up).
|
||||
|
||||
Since then, I learned about ledger-autosync and boobank, which I use to sync my bank statements with Ledger (more about that in another post).
|
||||
|
||||
### YNAB’s way of budgeting
|
||||
|
||||
I only came across [YNAB][3] recently. While I won’t use their software (being a non-free web application, and, you know… there’s no `M-x ynab`), I think that the principles behind it are really appealing for personal budgeting. I encourage you to [read more about it][4] (or grab a [copy of the book][5], it’s great), but here’s the idea.
|
||||
|
||||
1. **Budget every euro** : Quite simple once you get it. Every single Euro you have should be in a budget envelope. You should assign a job to every Euro you earn (that’s called [zero-based][6], [envelope system][7]).
|
||||
|
||||
2. **Embrace your true expenses** : Plan for larger and less frequent expenses, so when a yearly bill arrives, or your car breaks down, you’ll be covered.
|
||||
|
||||
3. **Roll with the punches** : Address overspending as it happens by taking money overspent from another envelope. As long as you keep budgeting, you’re succeeding.
|
||||
|
||||
4. **Age your money** : Spend less than you earn, so your money stays in the bank account longer. As you do that, the age of your money will grow, and once you reach the goal of spending money that is at least one month old, you won’t worry about that next bill.
|
||||
|
||||
|
||||
|
||||
|
||||
### Implementation in Ledger
|
||||
|
||||
I assume that you are familiar with Ledger, but if not I recommend reading its great [introduction][8] and [tutorial][9].
|
||||
|
||||
The implementation in Ledger uses plain double-entry accounting. I took most of it from [Sacha][10], with some minor differences.
|
||||
|
||||
#### Budgeting new money
|
||||
|
||||
After each income transaction, I budget the new money:
|
||||
```
|
||||
2018-06-12 Employer
|
||||
Assets:Bank:Checking 1600.00 EUR
|
||||
Income:Salary -1600.00 EUR
|
||||
|
||||
2018-06-12 Budget
|
||||
[Assets:Budget:Food] 400.00 EUR
|
||||
[Assets:Budget:Rent] 600.00 EUR
|
||||
[Assets:Budget:Utilities] 600.00 EUR
|
||||
[Equity:Budget] -1600.00 EUR
|
||||
|
||||
```
|
||||
|
||||
Did you notice the square brackets around the accounts of the budget transaction? It’s a feature Ledger calls [virtual postings][11]. These postings are not considered real, and won’t be present in any report that uses the `--real` flag. This is exactly what we want, since it’s a budget allocation and not a “real” transaction. Therefore we’ll use the `--real` flag for all reports except for our budget report.
|
||||
|
||||
#### Automatically crediting budget accounts when spending money
|
||||
|
||||
Next, we need to credit the budget accounts each time we spend money. Ledger has another neat feature called [automated transactions][12] for this:
|
||||
```
|
||||
= /Expenses/
|
||||
[Assets:Budget:Unbudgeted] -1.0
|
||||
[Equity:Budget] 1.0
|
||||
|
||||
= /Expenses:Food/
|
||||
[Assets:Budget:Food] -1.0
|
||||
[Assets:Budget:Unbudgeted] 1.0
|
||||
|
||||
= /Expenses:Rent/
|
||||
[Assets:Budget:Rent] -1.0
|
||||
[Assets:Budget:Unbudgeted] 1.0
|
||||
|
||||
= /Expenses:Utilities/
|
||||
[Assets:Budget:Utilities] -1.0
|
||||
[Assets:Budget:Unbudgeted] 1.0
|
||||
|
||||
```
|
||||
|
||||
Every expense is taken out of the `Assets:Budget:Unbudgeted` account by default.
|
||||
|
||||
This forces me to budget properly, as `Assets:Budget:Unbudgeted` should always be 0 (if it is not the case I immediately know that there is something wrong going on).
|
||||
|
||||
All other automatic transactions take money out of the `Assets:Budget:Unbudgeted` account instead of `Equity:Budget` account.
|
||||
|
||||
#### A Budget report
|
||||
|
||||
This is the final piece of the puzzle. Here’s the budget report command:
|
||||
```
|
||||
ledger --empty -S -T -f ledger.dat bal ^assets:budget
|
||||
|
||||
```
|
||||
|
||||
If we have the following transactions:
|
||||
```
|
||||
2018/06/12 Groceries store
|
||||
Expenses:Food 123.00 EUR
|
||||
Assets:Bank:Checking
|
||||
|
||||
2018/06/12 Landlord
|
||||
Expenses:Rent 600.00 EUR
|
||||
Assets:Bank:Checking
|
||||
|
||||
2018/06/12 Internet provider
|
||||
Expenses:Utilities:Internet 40.00 EUR
|
||||
Assets:Bank:Checking
|
||||
|
||||
```
|
||||
|
||||
Here’s what the report looks like:
|
||||
```
|
||||
837.00 EUR Assets:Budget
|
||||
560.00 EUR Utilities
|
||||
277.00 EUR Food
|
||||
0 Rent
|
||||
0 Unbudgeted
|
||||
--------------------
|
||||
837.00 EUR
|
||||
|
||||
```
|
||||
|
||||
### Conclusion
|
||||
|
||||
Ledger is amazingly powerful, and provides a great framework for YNAB-like budgeting. In a future post I’ll explain how I automatically import my bank transactions using a mix of `ledger-autosync` and `weboob`.
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://emacs.cafe/ledger/emacs/ynab/budgeting/2018/06/12/elbank-ynab.html
|
||||
|
||||
作者：[Nicolas Petton][a]
|
||||
选题：[lujun9972](https://github.com/lujun9972)
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]:https://emacs.cafe/l
|
||||
[1]:https://github.com/NicolasPetton/elbank
|
||||
[2]:https://www.ledger-cli.org/
|
||||
[3]:https://ynab.com
|
||||
[4]:https://www.youneedabudget.com/method/
|
||||
[5]:https://www.youneedabudget.com/book-order-now/
|
||||
[6]:https://en.wikipedia.org/wiki/Zero-based_budgeting
|
||||
[7]:https://en.wikipedia.org/wiki/Envelope_system
|
||||
[8]:https://www.ledger-cli.org/3.0/doc/ledger3.html#Introduction-to-Ledger
|
||||
[9]:https://www.ledger-cli.org/3.0/doc/ledger3.html#Ledger-Tutorial
|
||||
[10]:http://sachachua.com/blog/2014/11/keeping-financial-score-ledger/
|
||||
[11]:https://www.ledger-cli.org/3.0/doc/ledger3.html#Virtual-postings
|
||||
[12]:https://www.ledger-cli.org/3.0/doc/ledger3.html#Automated-Transactions
|
||||
55
translated/tech/20180606 6 Open Source AI Tools to Know.md
Normal file
55
translated/tech/20180606 6 Open Source AI Tools to Know.md
Normal file
@ -0,0 +1,55 @@
|
||||
应该知道的 6 个开源 AI 工具
|
||||
======
|
||||
|
||||
|
||||
|
||||
在开源领域，不管你的想法是多少的新颖独到，先去看一下别人是否已经做成了这个概念，总是一个很明智的做法。对于有兴趣借助不断成长的人工智能（AI）的力量的组织和个人来说，许多非常好的工具不仅是免费和开源的，而且在很多的情况下，它们都已经过测试和久经考验的。
|
||||
|
||||
在领先的公司和非盈利组织中，AI 的优先级都非常高，并且这些公司和组织都开源了很有价值的工具。下面的样本是任何人都可以使用的免费的、开源的 AI 工具。
|
||||
|
||||
**Acumos.** [Acumos AI][1] 是一个平台和开源框架，使用它可以很容易地去构建、共享和分发 AI 应用。它规范了需要的基础设施栈和组件，使其可以在一个“开箱即用的”通用 AI 环境中运行。这使得数据科学家和模型训练者可以专注于它们的核心竞争力，而不用在无止境的定制、建模、以及训练一个 AI 实现上浪费时间。
|
||||
|
||||
Acumos 是 [LF 深度学习基金会][2] 的一部分，它是 Linux 基金会中的一个组织，它支持在人工智能、机器学习、以及深度学习方面的开源创新。它的目标是让这些重大的新技术可用于开发者和数据科学家，包括那些在深度学习和 AI 上经验有限的人。LF 深度学习基金会 [最近批准了一个项目生命周期和贡献流程][3]，并且它现在正接受项目贡献的建议。
|
||||
|
||||
**Facebook 的框架.** Facebook 它自己 [有开源的][4] 中央机器学习系统，它设计用于做一些大规模的人工智能任务，以及一系列其它的 AI 技术。这个工具是经过他们公司验证的平台的一部分。Facebook 也开源了一个叫 [Caffe2][5] 的深度学习和人工智能的框架。
|
||||
|
||||
**说到 Caffe.** Yahoo 也在开源许可证下发布了它自己的关键的 AI 软件。[CaffeOnSpark 工具][6] 是基于深度学习的，它是人工智能的一个分支，在帮助机器识别人类语言、或者照片、视频的内容方面非常有用。同样地，IBM 的机器学习程序 [SystemML][7] 可以通过 Apache 软件基金会免费共享和修改。
|
||||
|
||||
**Google 的工具.** Google 花费了几年的时间开发了它自己的 [TensorFlow][8] 软件框架，用于去支持它的 AI 软件和其它预测和分析程序。TensorFlow 是你可能都已经在使用的一些 Google 工具背后的引擎，包括 Google Photos 和在 Google app 中使用的语言识别。
|
||||
|
||||
Google 开源了两个 [AIY kits][9]，它可以让个人很容易地使用人工智能，它们专注于计算机视觉和语音助理。这两个工具包将用到的所有组件封装到一个盒子中。这个工具包目前在美国的 Target 中有售，并且它是基于开源的树莓派平台的 —— 有越来越多的证据表明，在开源和 AI 交集中将发生非常多的事情。
|
||||
|
||||
**H2O.ai.** **** 我 [以前介绍过][10] H2O.ai，它在机器学习和人工智能领域中占有一席之地，因为它的主要工具是免费和开源的。你可以获取主要的 H2O 平台和 Sparkling Water，它与 Apache Spark 一起工作，只需要去 [下载][11] 它们即可。这些工具遵循 Apache 2.0 许可证，它是一个非常灵活的开源许可证，你甚至可以在 Amazon Web 服务（AWS）和其它的集群上运行它们，而这仅需要几百美元而已。
|
||||
|
||||
**Microsoft Onboard.** “我们的目标是让 AI 大众化，让每个人和组织获得更大的成就，“ Microsoft CEO Satya Nadella [说][12]。因此，微软持续迭代它的 [Microsoft Cognitive Toolkit][13]。它是一个能够与 TensorFlow 和 Caffe 去竞争的一个开源软件框架。Cognitive 工具套件可以工作在 64 位的 Windows 和 Linux 平台上。
|
||||
|
||||
Cognitive 工具套件团队的报告称，“Cognitive 工具套件通过允许用户去创建、训练、以及评估他们自己的神经网络，以使企业级的、生产系统级的 AI 成为可能，这些神经网络可能跨多个 GPU 以及多个机器在大量的数据集中高效伸缩。”
|
||||
|
||||
从来自 Linux 基金会的新电子书中学习更多的有关 AI 知识。Ibrahim Haddad 的 [开源 AI：项目、洞察、和趋势][14] 调查了 16 个流行的开源 AI 项目—— 深入研究了他们的历史、代码库、以及 GitHub 的贡献。 [现在可以免费下载这个电子书][14]。
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://www.linux.com/blog/2018/6/6-open-source-ai-tools-know
|
||||
|
||||
作者：[Sam Dean][a]
|
||||
选题：[lujun9972](https://github.com/lujun9972)
|
||||
译者：[qhwdw](https://github.com/qhwdw)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]:https://www.linux.com/users/sam-dean
|
||||
[1]:https://www.acumos.org/
|
||||
[2]:https://www.linuxfoundation.org/projects/deep-learning/
|
||||
[3]:https://www.linuxfoundation.org/blog/lf-deep-learning-foundation-announces-project-contribution-process/
|
||||
[4]:https://code.facebook.com/posts/1687861518126048/facebook-to-open-source-ai-hardware-design/
|
||||
[5]:https://venturebeat.com/2017/04/18/facebook-open-sources-caffe2-a-new-deep-learning-framework/
|
||||
[6]:http://yahoohadoop.tumblr.com/post/139916563586/caffeonspark-open-sourced-for-distributed-deep
|
||||
[7]:https://systemml.apache.org/
|
||||
[8]:https://www.tensorflow.org/
|
||||
[9]:https://www.techradar.com/news/google-assistant-sweetens-raspberry-pi-with-ai-voice-control
|
||||
[10]:https://www.linux.com/news/sparkling-water-bridging-open-source-machine-learning-and-apache-spark
|
||||
[11]:http://www.h2o.ai/download
|
||||
[12]:https://blogs.msdn.microsoft.com/uk_faculty_connection/2017/02/10/microsoft-cognitive-toolkit-cntk/
|
||||
[13]:https://www.microsoft.com/en-us/cognitive-toolkit/
|
||||
[14]:https://www.linuxfoundation.org/publications/open-source-ai-projects-insights-and-trends/
|
||||
@ -0,0 +1,66 @@
|
||||
Mesos 和 Kubernetes：不是竞争者
|
||||
======
|
||||
|
||||
|
||||
|
||||
Mesos 的起源可以追溯到 2009 年，当时，Ben Hindman 还是加州大学伯克利分校研究并行编程的博士生。他们在 128 核的芯片上做大规模的并行计算，并尝试去解决多个问题，比如怎么让软件和库在这些芯片上运行更高效。他与同学们讨论能否借鉴并行处理和多线程的思想，并将它们应用到集群管理上。
|
||||
|
||||
Hindman 说 "最初，我们专注于大数据” 。那时，大数据非常热门，并且 Hadoop 是其中一个热门技术。“我们发现，人们在集群上运行像 Hadoop 这样的程序与运行多线程应用和并行应用很相似。Hindman 说。
|
||||
|
||||
但是，它们的效率并不高，因此，他们开始去思考，如何通过集群管理和资源管理让它们运行的更好。”我们查看了那个时间很多的不同技术“ Hindman 回忆道。
|
||||
|
||||
然而，Hindman 和他的同事们，决定去采用一种全新的方法。”我们决定去对资源管理创建一个低级的抽象，然后在此之上运行调度服务和做其它的事情。“ Hindman 说，“基本上，这就是 Mesos 的本质 —— 将资源管理部分从调度部分中分离出来。”
|
||||
|
||||
他成功了，并且 Mesos 从那时开始强大了起来。
|
||||
|
||||
### 将项目呈献给 Apache
|
||||
|
||||
这个项目发起于 2009 年。在 2010 年时，团队决定将这个项目捐献给 Apache 软件基金会（ASF）。它在 Apache 孵化，并于 2013 年成为顶级项目（TLP）。
|
||||
|
||||
为什么 Mesos 社区选择 Apache 软件基金会有很多的原因，比如，Apache 许可证，以及他们已经拥有了一个充满活力的此类项目的许多其它社区。
|
||||
|
||||
与影响力也有关系。许多在 Mesos 上工作的人，也参与了 Apache，并且许多人也致力于像 Hadoop 这样的项目。同时，来自 Mesos 社区的许多人也致力于其它大数据项目，比如 Spark。这种交叉工作使得这三个项目 —— Hadoop、Mesos、以及 Spark —— 成为 ASF 的项目。
|
||||
|
||||
与商业也有关系。许多公司对 Mesos 很感兴趣，并且开发者希望它能由一个中立的机构来维护它，而不是让它成为一个私有项目。
|
||||
|
||||
### 谁在用 Mesos？
|
||||
|
||||
更好的问题应该是，谁不在用 Mesos？从 Apple 到 Netflix 每个都在用 Mesos。但是，Mesos 也面临任何技术在早期所面对的挑战。”最初，我要说服人们，这是一个很有趣的新技术。它叫做“容器”，因为它不需要使用虚拟机“ Hindman 说。
|
||||
|
||||
从那以后，这个行业发生了许多变化，现在，只要与别人聊到基础设施，必然是从”容器“开始的 —— 感谢 Docker 所做出的工作。今天再也不需要说服工作了，而在 Mesos 出现的早期，前面提到的像 Apple、Netflix、以及 PayPal 这样的公司。他们已经知道了容器化替代虚拟机给他们带来的技术优势。”这些公司在容器化成为一种现象之前，已经明白了容器化的价值所在“， Hindman 说。
|
||||
|
||||
可以在这些公司中看到，他们有大量的容器而不是虚拟机。他们所做的全部工作只是去管理和运行这些容器，并且他们欣然接受了 Mesos。在 Mesos 早期就使用它的公司有 Apple、Netflix、PayPal、Yelp、OpenTable、和 Groupon。
|
||||
|
||||
“大多数组织使用 Mesos 来运行任意需要的服务” Hindman 说，“但也有些公司用它做一些非常有趣的事情，比如，数据处理、数据流、分析负载和应用程序。“
|
||||
|
||||
这些公司采用 Mesos 的其中一个原因是，资源管理层之间有一个明晰的界线。当公司运营容器的时候，Mesos 为他们提供了很好的灵活性。
|
||||
|
||||
“我们尝试使用 Mesos 去做的一件事情是去创建一个层，以让使用者享受到我们的层带来的好处，当然也可以在它之上创建任何他们想要的东西，” Hindman 说。 “我认为这对一些像 Netflix 和 Apple 这样的大公司非常有用。”
|
||||
|
||||
但是，并不是每个公司都是技术型的公司；不是每个公司都有或者应该有这种专长。为帮助这样的组织，Hindman 联合创建了 Mesosphere 去围绕 Mesos 提供服务和解决方案。“我们最终决定，为这样的组织去构建 DC/OS，它不需要技术专长或者不想把时间花费在像构建这样的事情上。”
|
||||
|
||||
### Mesos vs. Kubernetes?
|
||||
|
||||
人们经常用 x 相对于 y 这样的术语来考虑问题，但是它并不是一个技术对另一个技术的问题。大多数的技术在一些领域总是重叠的，并且它们可以是互补的。“我不喜欢将所有的这些东西都看做是竞争者。我认为它们中的一些与另一个在工作中是互补的，” Hindman 说。
|
||||
|
||||
“事实上，名字 Mesos 表示它处于 ‘中间’；它是一种中间的 OS，” Hindman 说，“我们有一个容器调度器的概念，它能够运行在像 Mesos 这样的东西之上。当 Kubernetes 刚出现的时候，我们实际上在 Mesos 的生态系统中接受它的，并将它看做是运行在 Mesos 之上、DC/OS 之中的另一种方式的容器。”
|
||||
|
||||
Mesos 也复活了一个名为 [Marathon][1]（一个用于 Mesos 和 DC/OS 的容器编排器）的项目，它在 Mesos 生态系统中是做的最好的容器编排器。但是，Marathon 确实无法与 Kubernetes 相比较。“Kubernetes 比 Marathon 做的更多，因此，你不能将它们简单地相互交换，” Hindman 说，“与此同时，我们在 Mesos 中做了许多 Kubernetes 中没有的东西。因此，这些技术之间是互补的。”
|
||||
|
||||
不要将这些技术视为相互之间是敌对的关系，它们应该被看做是对行业有益的技术。它们不是技术上的重复；它们是多样化的。据 Hindman 说，“对于开源领域的终端用户来说，这可能会让他们很困惑，因为他们很难去知道哪个技术适用于哪种负载，但这是被称为开源的这种东西最令人讨厌的本质所在。“
|
||||
|
||||
这只是意味着有更多的选择，并且每个都是赢家。
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://www.linux.com/blog/2018/6/mesos-and-kubernetes-its-not-competition
|
||||
|
||||
作者：[Swapnil Bhartiya][a]
|
||||
选题：[lujun9972](https://github.com/lujun9972)
|
||||
译者：[qhwdw](https://github.com/qhwdw)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]:https://www.linux.com/users/arnieswap
|
||||
[1]:https://mesosphere.github.io/marathon/
|
||||
Loading…
Reference in New Issue
Block a user