YouSaid · the spoken record

Travis Oliphant

lines on the record: 265
first: 2021-09-23
most recent: 2021-09-23
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“So labs is, you know, I think there should be a lot of things like quansite labs. I think this concept is one that scales. You could have a lot of open source research labs. Along the way, so in 2018, when the bigger idea came how to make open source investor, I said, oh, I need to write, I need to create a venture fund. So we created a venture fund called QuanSet Initiate at the same time. It's an angel fund, really. We started to learn that process. How do we actually do this? How do we get LPs? How do we actually go in this direction and build a fund? And I'm like, every venture fund should have an associated open source research lab. There's just no reason. Like our venture fund, the carried interest. A portion of it goes to the lab. It directly will fund the lab”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And we just didn't structure it so they could. But in fact, they have. You look at Basque, there's two companies coming out of Dasque. Boke could be a company. There's like lots of companies that could exist off the work we did there. And so I thought, oh, here's a recipe for an incubation, a concept that we could actually spawn new companies and new innovations. And then the idea has always been, well, money they earn should come back to fund the open source project.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“So OpenTeams, I'm super excited about Open Teams because it's one of the, I mentioned my idea for investing directly in open source. So that's a concept called FaroSS. But one of the things when we started Quantite, we knew we would do is we develop products and ideas and new companies might come out. And Anaconda, this was clear. Anaconda, we did so much innovation that like five or six companies could have come out of that.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I love the folks. I really love the folks at TensorFlow, too. They're fantastic. I think it's the just. How it integrates with their business. I mean, like I said, there's a lot of reasons just the timing, the integration with their business, what they're looking for. They're probably looking for more users. And I was looking to kind of keep some development effort and they couldn't receive that as easily, I think. So I'm hoping, I'm really hopeful and love the people there.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I'm PyTorch, right? So I basically started Quantz that I went to both TensorFlow and PyTorch and said, Hey, I want to help connect what you're doing to the broader SciPy ecosystem. Because I see what you're doing, but we have this bigger mission that we want to make sure we don't, you know, lose energy here. And Facebook responded really positively and I didn't get the same reaction.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Num I would love to talk to him. In fact, we have at Quancett, we've been fortunate enough to work with Facebook on PyTourse directly. So we have about 13 developers at QuanSite. Some of them are in labs working directly on PyTorch.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Correct, absolutely, absolutely. GPUs are absolutely one of them. And new hardware beyond GPUs. I mean, Tesla's dojo chip. I'm hoping we'll have a chance to work on that perhaps. Things like that are definitely driving it. The other thing is driving is scalable. Speed and scale. How do I write numpy code or numpy like code if I want it to run across a cluster? Know, oh, that's Dask, or maybe it's Ray. I mean, there's sort of ways to do that now, or there's Modin, and there's Pandas code, numpy code, sci-py code, second learn code that I want to scale. So that's one big area.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“So, Labs is working to make the software better, like make NumPy better, make SciPy better. It only works on open source. If somebody wants to, so you know, companies do. We have a thing called a community work order, we call it. If a company says, I want to make Spider better, okay, cool. You can pay for a month of a developer of Spider or a developer of NumPy or developer of SciPy, you can't tell them what you want them to do. You can give them your priorities and things you wish existed, and they'll work on those priorities with the community to get what the community wants and what emerges with what the community wants”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Create a place where maintainers so that SciPy and NumPy and Numba and all these projects we already started can pay people to work on them and keep them going. So that's labs. QuanSite Labs is a separate organization. It's a nonprofit mission. The profits of QuanSite help fund it. In fact, every project that we have at QuanSite, a portion of the money goes directly to QuanSite Labs to help keep it funded. So we've got several mechanisms we keep Quansite Labs funded. Currently, so I'm really excited about labs because it's been a mission for a long time. So what kind of projects?”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Full thing will help you build an infrastructure if you're using Jupyter. We do staff augmentation, you need more programmers, help you use Das more effectively, help use GPUs more effectively. Just basically a lot of people need help. So we do training as well to help people both Immediate help and then learn from somebody. We've added a bunch of stuff too. We've kind of separated some of these other things into another company called Open Teams that we currently started. One of the things I loved about we did at Anaconda was creating a community innovation team. And so I want to replicate that. This time we did a lot of innovation in Anaconda. I wanted to do innovation, but also contribute to the projects that existed.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Big picture fun site. Fun site is mission to connect data to an open economy. So it's basically consulting of the Pi Data ecosystem. It's a consulting company. And what I've said when I started it is we're trying to create products, people, and technology. So it's divided into two groups. And a third one as well. The two groups are a consulting services company that just helps people do data science and data engineering and data management better and more efficiently.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Was larger and had more money because I know tons of things to do effectively with more resources. But I have not yet been successful at channel tons of it. Some, you know, I'm happy with what we've done. We've created again at QuanSite what we created to get Anaconda started. We created community Anaconda started, done it again with QuanSite. Super excited by that. It took for years to do it.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“So let's give up and allow a system packager to work. That way Anaconda is installed and it has PIP. It would default to Condo to install its stuff. But Red Hat RPM would default to RPM to install more things. Like that's a key, not difficult, but somewhat some work feature needs to be added. That's an example of something like I've known we need to do it. I mean, it's where I wish I had more money. I wish I was more successful in the business side, trying to get there, but I wish my, you know.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“The thing they did really well is Python developers who want to get their stuff published, you have to have a pip recipe. Yeah. Right? I mean, even if it's, you know, the challenge is, and there's a key thing that needs to be added to PIP, just simply add the PIP the ability to defer to a system package manager. Because recognize you're not going to solve all the dependency problem.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And then Pippinstall on top of that, that's fine. Be careful about PIP installing OpenCV or TensorFlow. Because if somebody's allowed that, it's going to be most surely done in a way that can't be updated that easily.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“But what I recommend now is a hybrid. I do. I mean, I have no problem. Is it possible to use? Oh, it is. It is. What I like the environment with Pip with conda? Build an environment with conda?”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And we still use it all the time at QuanSite and with other clients. But you can kind of do similar things with Pippen Docker. Especially the web development community, part of it, again, is there's a lot of different kind of developers in the Python ecosystem. And there's still a lack of some clear understanding. I go to the Python conference all the time and there's only a few people in the PyPA who get it. And then others who are just massively trumpeting the power of PIP but just do not understand the problem.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“It is confusing. It's really a matter of kind of have some challenges, first of all, kind of still needs to be improved. There's lots of improvements to be made. And it's that aspect of, wait, who's doing this? And the fact that then the PIPA really stepped up. Like they were not solving the problem at all. And now they kind of got to where they're solving it for the most part. And then effectively you could get like Konda solved a problem that was there. And it still does. And it's still, you know, there's still great things it can do.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Peter Solana Conde, right? We're still great friends, we're great friends. We talk all the time. I love him to death. There's a long story there about why and how we can cover in some other podcast, perhaps. It's sort of a more business focused one. But this is one area where I think conda should be more community driven. He should be pushing more to get more community contributors to conda and let the... Anaconda shouldn't be fighting this battle. Right. It's actually a developer's. Like you said, help the developers, and then they'll actually move us the right direction.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“But ultimately, for projects to succeed virally and become massive influencers, they have to get community people on board. They have to get other people on board. So it has to become community driven. And a big part of that is engagement with those people, empowering people, governance around it. And what happened with Kanda in the early days, PIP emerged, and we did do some good things, kinda forge, conda forage community is sort of the community recipe creation community. The Kant itself, I am still believe, and Peter is CEO of Anacondi, he's my co founder, I ran Anaconda until 2017, 2018.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Pip generally hasn't thought deeply about the binary dependency problem, right? And that's why fundamentally it doesn't work for the SciPa ecosystem. You can sort of paper over it and duct tape and it kind of works until it doesn't and it falls apart entirely. So it's been a mixed bag. And I've been having lots of conversations with people over the years because again, it's an area where if you understand some things, but not all the things, but they've done a great job of community appeal. This is an area where I think Anaconda as a company needed to do some things in order to make condom more community centric, right? And this is something, I talk about this all the time. There's a balance between you have every project starts with what I call company-backed open source, even if the company is yourself, it's just one person doing business ads.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“So, Condor has a notion of variance of a package. You can actually have different compilation versions of a package. So not just the versions different, but oh, this is compiled with these optimizations. So Condor does have an answer. Has flavors basically. Well,”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“She's as involved. So many dependencies, right So our use case of conda was conda install science. And it was the best way to install PsykkitLearn in 2013 to really 2018, 17, 18. Pip finally caught up. I still think you should kind of install PsykkitLearn to put a pip install PsychicLearn, but you can PIP install ScikitLearn. The issue is the package they created was Wheels, and PIP does not handle the multi-vendor approach. They don't handle the fact you have C++ libraries, you're depending on. They just stop at the Python boundary. And so what you have to do in the wheel world is you have to vendor. You have to take all of the binary and vendor it. Now if your change happens on underline dependency, you have to redo the whole wheel. So TensorFlow is a good example of a, you should not pip install TensorFlow. It's a terrible idea. People do it because the popularity of PIP, many people think, oh, of course that's how I install everything Python.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“A lot of people in Enria, a lot of people, like a lot of European contributors, Andreas, there's some Andreas in the US. There's a lot of just people I just adore, I think are amazing people. Awesome use of sci-fi, right? I love the fact that they were using sci-fi effectively to do something I loved, which is machine learning, but couldn't install it.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And it was a fantastic move. The community did. I didn't do it. I was like, okay, that's a good idea. I didn't like the name. I didn't like the fact you typed sidekit image. I was like, that's got to be simpler. SK learn. We got to make that smaller. I don't like typing all this stuff imports. So I was kind of a pressure that way. But I love the energy and love the fact they went out and they did it. And lots of people, Jared Millman, and then, of course, Gael, and there's people I'm not even naming. Psykkit Learn really emerged as fantastic project.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“We released Anaconda, which was just a distribution of libraries, but we started work on conda in 2012. First version of Conda came out in early 2013, summer of 2013, and it was a package manager. So you could say Conda installs Scikit Learn. In fact, that was the Scikit Learn was a fantastic project that emerged. It was the classic example of the scikits. I talked to you earlier about SciPy being too big to be a single library. Well, what the community had done is said, let's make sci-kits and their psychit image, their psychkit learn. There's a lot of scikits.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Correct exactly. That was the world we faced, and we decided to go multiple operating systems, multiple and programming language independent. Because even Python, in particular what was important, was SciPy has a bunch of Fortran in it, right? And scikit-learn has links to a bunch of C++. There's a lot of compiled code. And the Python package managers, especially early on, didn't even support that. So in 2000, so we.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“And reinstalling such a pain, don't do it. I'm like, wait, okay, so now we're not making changes to a library because of the installation problem that'll cause for end users. Okay, there's a problem with installation. We've got to fix this. So we said we're going to make a distribution in Python. And we'd previously done that. I'd previously done that at NTOT. I wanted to make one that would give away for free everyone could just get. It was critical that we could just get it. It wasn't tied to a product. It was just you could get it. And then we had constantly thought about, well, do we just leverage RPM? But the challenge had always been we want a package manager that works on Windows, Mac OS X, and Linux the same. And it wasn't there. Like, you don't have anything like that.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“All right, you said, okay, you said to do this ourselves. At the same time, people did start to work on the packaging story in Python. It just took a little longer. So in 2012, kind of motivated by our training courses we were teaching, like very similar to what you just mentioned about your mother. It was motivated by the same. Purpose. Like, how do we get this into people's hands? And it's this big, long process. It takes too expensive. It was actually hurting NumPy development because I would hear people were saying, don't make that change to NumPy because I just spent a week getting my Python environment. And if you change Numpy, after reinstall everything.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Probably like the default thing you learn. It's the default user, yeah. It's where the story there emerged because what happened is in 2012, we had this meeting at the Google Plex and Guido was there to come talk about what are we going to do? How are we going to make things work better? Wes McKinney, me, Peter. Peter has a great photo of me talking to Guido, and he pretends we're talking about this story. Maybe we were maybe more, but we did at that meeting talk about it and asked Guido. Guido, we need to fix packaging in Python. People can't get the stuff. He said, go fix it yourself. I don't think we're going to do it.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Development tools that relate to package managers. But then there's a very specific user story around package management that those language specific package managers have to interact with and currently aren't doing a good job of that. That was one of the challenges that not seen that difference and still exists in the difference today. Conda always was a user. I'm going to use Python to do data science. I'm going to use Python to do something. How do I get this installed? It was always focused on that. So it didn't have like a develop, you know, classic example is PIP has a PIP develop. It's like I want to install this into my current development environment today now. Kind of doesn't have that concept because it's not part of the story.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“It is appropriate to have development tools. And there's an aspect of development tool that is related to packaging. And every language should have some story there to help their developers create.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“That's what I'm going to get to actually. This is exactly the journey I'm on is to sort of explain packaging in Python. I think it's best expressed. The conversation I have with Guido at a conference where I said, so yeah, packaging is kind of a problem in Guido said I don't ever care about packaging. Don't use it. I don't install new libraries. I'm like, I guess if you're the language creator and if you need something, you just put it in the distribution. Maybe you don't worry about packaging. But Guito has never really cared about packaging, right? And never really cared about the problem of distribution. Somebody else's problem. And that's a fair position to take, I think, as a language creator. In fact, there's a philosophical question about, should you have different development packaging managers? Should you have a package manager per language? Is that really the right approach? I think there are some answers of...”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“How did that came about? Yeah, conda, it was early on, like I said, with SciPy. SciPy was a distribution masquerading library. And he heard me talking about compiler issues and trying to get the stuff shipped and the fact that people can use your libraries if they have it. So for a long time, we'd understood the packaging problem in Python. And one of the first things you did at continuum analytics became Anaconda was organized the PyData ecosystem in conjunction with Numfocus. We actually started Numfocus with some other folks in the community the same year we started Anaconda. I said, we're going to build a corporation, but we also got to reify the community aspect and build a nonprofit. Can we do both of those?”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Foundational numerical work. So Boke got funded. Fortunately, Chris let us use some of the money to fund still some of that other fundational But it wasn't as, yeah, his hands were tired. He couldn't do anything about it. That was a whole interesting story.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Panda started the idea of here's a data frame on a dot plot. I'm just going to attach plot as a method to my object, which was a little bit controversial, right? But works pretty well, actually, because there's a lot less you have to pass in, right? You could just say, here's my object, you know what you are, you tell the. Visualization what to do so that and there's things like that that have not been super well developed entirely but Boke was focused on And DARPA has a very strict cutoff window. And so we had two proposals, one for the boke and one for actually Numba and the other work.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“I think we've got a pretty good API story around certain use cases of plotting. But there's a difference between static plots versus interactive plots versus I'm an end user. I just want to write a simple.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Look at us plotting. So Boke was one of the foundational things to say, I want to do plot in Python, but have the things show up in a web.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Dask number bouquet conda. There was a data shader, panel, hollow is. These are all tools that are extremely relevant in terms of helping you build applications, build tools, build faster code. There's a couple of JupyterLab. Jupyter Lab came out of this too.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“So that's what happened in Anaconda. There were lots of things we did not well in terms of that structure, and I've learned from since and how to do it better. But we did a really good job of kind of attracting the interest around the area to get good people working and then get funnel some money on some interesting projects. Super excited about what came out of our energy there. Like a lot did.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“So it's a little different than if you take money from a venture fund. If you take money from a venture fund, the venture fund, they want you to go big or go home. And they're kind of like expecting nine out of ten to fail or 990 to fail. It's different. I was at a barbell strategy. I was like, I can't fail. I mean, I may not do super well, but I cannot lose their money. So, I'm going to do something my mill can return a profit, but I want to have exposure to an upside.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“But these need to run at scale with lots of machines. The other thing we wanted to do was make user interfaces that were web. We wanted to make sure the web did not pass by the Python community, that we had ways to translate your data science to the web. So those are the two kind of technical areas. We thought, oh, we'll build products in this space. And that was the idea. Very quickly in, but of course the thing I knew how to do was to do consulting to make money and to make sure my family and friends and fools that had invested didn't lose their money.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I'll tell you a little bit of the history of that because Anaconda, we wanted to do Python because Peter and I had the goal of when we started Anaconda. We actually started as continuum analytics was the name of the company that started. It got renamed Anaconda in 2015. But we said we want to scale analytics. NumPy's great. Panda's is emerging.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Exactly There's been a couple things you know people will pay for. One, they'll pay for really good user interfaces, right? And so I'm always looking for what other things people will pay for that you can actually adapt to the open source infrastructure. One is definitely user interfaces. The second is speed. A better runtime, faster runtime.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“So we had the very first Kudajit and the very first at-jit compiler that in 2012, you could run not just a viewfunk on CPU, but a view funk on GPUs. That's awesome. It would automatically paralyze it and get 1,000 X speedup.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes. That was good chits. I completely agree. I'm very impressed. But number was an effort to make that happen with Python. And so we used some of the money we raised from Anaconda to do it. And then we also applied for this DARPA grant and used some of that money to continue the development. And then we used proceeds from service projects we would do. We get consulting projects on that we would then use some of the profits to invest in Numba. So we ended up with a team of two or three people working on Numba. It was a fits and starts, right? And ultimately the fact that we had a commercial version of it also we were writing. So part of the way I was trying to fund numbers say, well, let's do the free number and then we'll have a commercial version of number called number pro. And what number pro did is it targeted GPUs.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“People You, yeah, just in time compilation. They're actually really sophisticated. In fact, I got jealous of how much effort was put into the JavaScript jets.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Love Chris, but the name makes you imply that the virtual machine is what it's all about. It's actually the IR and the library, the code generation. The real beauty of it. The fact that what I love about LVM was the fact that it was a plateau you could collaborate on. Right instead of the internals of GCC or the internals of the Intel compiler, like, how do I extend that? And it was a place we could collaborate. And we were early, I mean, people had started before. It's a slow compiler. Like it's not a fast compiler. So for some kind of JITs, like JITs are common in language because one, every browser has a JavaScript JIT. It does real-time compilation of the JavaScript to machine code.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“It's really badly named low level virtual machine, which that part of it is not used. It's really low level. She doesn't mean.”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so Python, but then the whole goal of Numba is to translate Python bytecode to LLVM. And so LVM actually does the code generation. In fact, a lot of times they'd say, yeah, it's super easy to write a compiler. If you're not writing the parser nor the code generator, right?”
2021-09-23 · Lex Fridman Podcast · #224 – Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming · IDENTIFIED FROM THE TRANSCRIPT · source