Doughnut-shaped surface of revolution
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Straight from the goop, it's the beginning of that weird liminal time between books. We encourage you to check out our Patreon and/or Ko-Fi, as they've got sweet sweet benefits and also you can help us get to our goals--we're making great progress towards full episode transcripts! AND Our Store is a thing, with all your t-shirts, tote bags, stickers and more! Background music and sound effects: Music: Yesteryears (DECISION) by Sascha Ende Link: https://filmmusic.io/en/song/244-yesteryears-decision Torus, and Outriders Zak River Town, Medieval Market, and Forest: Day Tabletop Audio https://tabletopaudio.com Cavernous Deep Cave Ambience with Light Rhythmic Elements The Hollywood Edge https://hollywoodedge.com Email us at PodAgainsttheMachine@gmail.com Remember to check out https://podagainstthemachine.com for show transcripts, player biographies, and more. Stop by our Discord server to talk about the show: https://discord.gg/TVv9xnqbeW Follow @podvsmachine on Twitter Find us on Reddit, Instagram, and Facebook as well.
WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the rest of this episode.You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-themeHosts:eric beal - instagram.com/ericbealart/Josh Campbell - instagram.com/oatmeal_pizza_band
On Episode 210, Eric has a provocative theater experience, and Josh talks about the latest data published by the Dark Energy Survey. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorusYou can find Eric's research notes for every episode here:https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link:http://buenotornado.bandcamp.com/track/riding-the-torus-themeHosts:eric beal -instagram.com/ericbealart/Josh Campbell -twitter.com/josh_campbell
PATREON EXCLUSIVEOn Episode 209, Josh went on a whirlwind Broadway show tour and regales Eric with starry eyed tales of dreams coming true.WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the rest of this episode.You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-themeHosts:eric beal - instagram.com/ericbealart/Josh Campbell - twitter.com/josh_campbell
Lou a toujours beaucoup souffert pendant ses règles. Des examens, des diagnostics plus ou moins précis, différents moyens de contraception pour calmer les douleurs, à 22 ans, Lou est incollable sur les règles douloureuses. Mais cet entretien avec celle que ses patientes surnomment Docteur Amina va peut-être lui faire office de révélation…Amina Yamgnane est gynécologue et obstétricienne, experte régulièrement présente dans l'émission Ça Commence Aujourd'hui, aux côtés de Faustine Bollaert sur France 2. Son franc parler, sa vision iconoclaste de sa spécialité en font une figure majeure des acteurs de la santé. Un vendredi sur deux, retrouvez Docteur Amina Yamgnane et son invitée pour mettre en lumière les mystères du corps de la femme. L'endométriose, le déni de grossesse, le suicide post partum, les violences obstétricales, l'excision, la prématurité, la prévention du cancer du sein sont autant de thèmes que la gynécologue abordera avec écoute et bienveillance, informations et conseils, mais aussi avec toute sa personnalité, son humour, ses émotions et ses coups de gueule.Retrouvez Amina Yamgnane sur Instagram : @draminayamgnaneUne production Réservoir Prod / Médiawan / France Télévision Retrouvez également Amina Yamgnane dans Ça commence aujourd'hui en vidéo sur france·tv : https://www.france.tv/france-2/ca-commence-aujourd-hui/
PATREON EXCLUSIVE:On Episode 208, Eric watches a movie! We discuss Richard Linklater's 1990 independent film, "Slacker"WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the rest of this episode.You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-themeHosts:eric beal - instagram.com/ericbealart/Josh Campbell - twitter.com/josh_campbell
PATREON EXCLUSIVE:On Episode 207, Eric tells Josh about the history of Austin, Texas and the dramatic changes it has experienced over the last 25 years.WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the rest of this episode.You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-themeHosts:eric beal - instagram.com/ericbealart/Josh Campbell - twitter.com/josh_campbell
On Episode 206, Eric and Josh discuss the recently discovered Asteroid 2024 YR4 that is on a potential collision course with Earth in 2032. Plus Luka talk.WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorusYou can find Eric's research notes for every episode here:https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link:http://buenotornado.bandcamp.com/track/riding-the-torus-themeHosts:eric beal -instagram.com/ericbealart/Josh Campbell -twitter.com/josh_campbell
PATREON EXCLUSIVE: On Episode 205, Eric and Josh discuss the 1986 failed launch of the Space Shuttle Challenger. What went wrong, the culture of the shuttle program, the investigation and congressional hearings. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
After six long weeks and on time at all, we rejoin our battle already in progress! We encourage you to check out our Patreon and/or Ko-Fi, as they've got sweet sweet benefits and also you can help us get to our goals--we're making great progress towards full episode transcripts! AND Our Store is a thing, with all your t-shirts, tote bags, stickers and more! Background music and sound effects: Deep Slow Pulsing Breathing Drone The Hollywood Edge https://hollywoodedge.com Sepulcrum, Torus, Outriders, and Under the Hill Battle Theme Zak Email us at PodAgainsttheMachine@gmail.com Remember to check out https://podagainstthemachine.com for show transcripts, player biographies, and more. Stop by our Discord server to talk about the show: https://discord.gg/TVv9xnqbeW Follow @podvsmachine on Twitter Find us on Reddit, Instagram, and Facebook as well.
On Episode 204, Eric and Josh discuss the Parker Solar Probe. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
Click here to join the priority waitlist for the Awakened Mastery Journey! Awakened...Now What?! EPISODE 405 Tyler Engle, A.K.A BASSFORGE, is a multi-talented DJ, music producer, and metaphysician devoted to helping individuals unlock the mysteries of reality. Blending spiritual wisdom, ancient history, and cutting-edge sciences, Tyler distills complex concepts into practical insights that empower others to reach their highest potential. Whether through his transformative music or thought-provoking teachings, Tyler inspires seekers to harmonize mind, body, and soul, creating a life of limitless creativity and purpose. (00:00) Introduction (01:50) What triggered Tyler's exploration into metaphysics (06:05) Instagram Reaction - Cymatics (07:22) How can we achieve higher states of consciousness through music? (11:20) How is music evolving to adapt to consciousness? (14:25) How can music unlock dormant light codes within your DNA? (17:08) How does water shape our reality? (19:07) Does our brain build reality? (30:51) Is it Jiff or Jiffy? (31:25) Instagram reaction - Phi (35:46) What is the Torus field? (40:40) What is the sacred geometry of dimensions? (45:19) Clairvoyance practice (54:25) Instagram Reaction - Memory of Water (56:04) Now What?! (58:32) Memory Check Contact Bassforge here! @bassforge.us
Welcome to 2025! On Episode 203, Eric gives his on the ground reporting of the fires in LA, plus "The Rehearsal" and an anecdote about the Sun. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
During Hulaween festival 2024, Chris Dyer had the chance to sit down for an in-depth mystical conversation with artist friend Torus Energy aka Jack Lightfoot. Jack shares the meaning behind the name Torus Energy, his artwork, the inspiring interplay between dark and light, dream realm visions and channelings. The two also discuss the rapidly evolving trajectory of humanity towards singularity, AI, consciousness, how they are learning from what they are observing and how to navigate. About the Artist: Jack William Lightfoot under the pseudonym Torus energy is a multifaceted artist and creator from the UK born in 1992. Projects include painting, fashion design, architecture, carpentry, digital design and many others. Born in the Midlands countryside and exploring world's in search of constant inspiration to the work he creates with these experiences attempts to represent powerful unification of all the human spirits and in turn with the natural world and our part to play in it. Website: www.torusenergyart.com Social Media (IG): @thetorusenergy --- Support this podcast: https://podcasters.spotify.com/pod/show/chris-dyer8/support
Charlie Ziese is a visionary in the field of pyramid science and sacred geometry. As the owner and creator of Stargate Pyramids and Chairman of the Pyramid Science Foundation, Charlie is at the forefront of exploring the profound energies and organizing principles that govern our Universe. He is the author of the book, "76.345 - Exploring the Hidden Secrets of the Golden Ratio”, which demonstrates the existence of a process he calls Golden Ratio Scaling in ancient and contemporary architecture, pyramids, hieroglyphs, temples, churches, ancient diagrams, symbols, obelisks, monuments, and numerous natural phenomena.Charlie has also produced several significant research documentaries. "The Golden Ratio and the Organizing Principles of the Universe" delves into the mysteries of pyramid energy fields, Sacred Geometry, and free energy. This comprehensive work reveals how creation itself utilizes specific geometries such as Golden Ratio Scaling to manifest reality, offering viewers an awe-inspiring journey through the fundamental structures and processes that shape our world. He also presents a model of the Organizing Principles of the Universe which links the Torus with Golden Ratio Scaling, fractal scaling, the Fibonacci Sequence and Platonic Solid Scaling.In 2023, Charlie produced a documentary entitled “The Powers of Phi – The Golden Ratio in Gothic Cathedral Steeples”. In this documentary, Charlie demonstrates that, unbeknownst to the very church organizations that own them, every Gothic Cathedral Steeple utilizes one of six exponential powers of Phi geometries, demonstrating that as recently as 500 years ago, the great builders and architects had a highly sophisticated understanding of the Golden Ratio far surpassing that in contemporary society. Charlie's most recent research is contained in his 2024 documentary: “The Golden Ratio in Fluid Dynamics”. This cutting edge research demonstrates that Golden Ratio Scaling is found in the nose cones and other body parts of all of the fastest flying birds, fastest swimming sea animals, airplanes, helicopters, ancient aerodynamics dating back at least 70,000 years, commercial and private jets, missiles, rockets, spacecraft, racing cars, bullet trains, hypersonic vehicles, and numerous free energy technologies of the 19th and 20th centuries. Research into rocket and hypersonic engine exhaust nozzles corroborates the connection between Golden Ratio Scaling and free energy implosion physics. This research has far-reaching implications, as the research points to GRS as a “preferred pathway” programmed into the fabric of the Aether.https://pyramidsciencefoundation.org/Become a supporter of this podcast: https://www.spreaker.com/podcast/earth-ancients--2790919/support.
On Episode 202, the boys are back for another year end Bonenkai with special guests TC Fleming and Justin Bell. Let's imbibe to forget all the turmoils of 2024 and revel in the ensuing chaos! WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
Your torus field is an electromagnetic energy system connected to your 7 chakras and aura which is based in the heart. As we complete powerful transition periods - especially Pluto in Capricorn and the Nodes of Fate resetting a 19-year cycle -you may feel a strong renewal in yourself around people you can no longer see as an energetic disconnect has occurred. We have experienced many significant frequency shifts that can feel incremental at times, and then we finally notice how far we have come over an extended period of time. Your regenerated toroidal field is confirmation of your deep changes, as well as feeling grounded in your light body and the new incoming energy streams. Much more to share in this podcast episode. ~ Aquarian Diamond Lightcode Meditation: In this guided visualization, you will connect with more of your cosmic talents, flush out and release expired cellular energy, call in more of your star family connections and gifts from other dimensions, embed the earth's ley lines with higher cosmic frequencies, re-align your relationships with solar consciousness, and immerse into newly encoded diamond frequency potentials. Check it out: https://www.mollymccord.online/store/Fcu9MhR8
A brain without a dragon is still pretty dang dangerous. We encourage you to check out our Patreon and/or Ko-Fi, as they've got sweet sweet benefits and also you can help us get to our goals--we're making great progress towards full episode transcripts! AND Our Store is a thing, with all your t-shirts, tote bags, stickers and more! Background music and sound effects: Deep Slow Pulsing Breathing Drone The Hollywood Edge https://hollywoodedge.com Sepulcrum, Torus, Outriders, and Under the Hill Battle Theme Zak Email us at PodAgainsttheMachine@gmail.com Remember to check out https://podagainstthemachine.com for show transcripts, player biographies, and more. Stop by our Discord server to talk about the show: https://discord.gg/TVv9xnqbeW Follow @podvsmachine on Twitter Find us on Reddit, Instagram, and Facebook as well.
This week we dive into the power of the heart and the flow of the torus field, and how it helps us create or heal pain and dis-ease. Connect with Katie on Instagram: @holistickatie_ @marramovement --- Support this podcast: https://podcasters.spotify.com/pod/show/living-integrated/support
The Stanford Torus is a vast space habitat design intended to allow thousands to dwell safely and comfortably in space, but how do we build such a structure?Go to https://buyraycon.com/isaacarthur to get up to 30% off sitewide! Brought to you by Raycon.Visit our Website: http://www.isaacarthur.netJoin Nebula: https://go.nebula.tv/isaacarthurSupport us on Patreon: https://www.patreon.com/IsaacArthurSupport us on Subscribestar: https://www.subscribestar.com/isaac-arthurFacebook Group: https://www.facebook.com/groups/1583992725237264/Reddit: https://www.reddit.com/r/IsaacArthur/Twitter: https://twitter.com/Isaac_A_Arthur on Twitter and RT our future content.SFIA Discord Server: https://discord.gg/53GAShECredits:The Stanford Torus Space HabitatEpisode 475; November 28, 2024Produced, Narrated & Written: Isaac ArthurEditor: Ludwig LuskaGraphics:Bryan VersteegFishy TreeJermey JozwikKen York YD VisualNeil BlevinsSegio BoteroUdo SchroeterSelect imagery/video supplied by Getty ImagesMusic Courtesy of Epidemic Sound http://epidemicsound.com/creatorSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
The Stanford Torus is a vast space habitat design intended to allow thousands to dwell safely and comfortably in space, but how do we build such a structure?Go to https://buyraycon.com/isaacarthur to get up to 30% off sitewide! Brought to you by Raycon.Visit our Website: http://www.isaacarthur.netJoin Nebula: https://go.nebula.tv/isaacarthurSupport us on Patreon: https://www.patreon.com/IsaacArthurSupport us on Subscribestar: https://www.subscribestar.com/isaac-arthurFacebook Group: https://www.facebook.com/groups/1583992725237264/Reddit: https://www.reddit.com/r/IsaacArthur/Twitter: https://twitter.com/Isaac_A_Arthur on Twitter and RT our future content.SFIA Discord Server: https://discord.gg/53GAShECredits:The Stanford Torus Space HabitatEpisode 475; November 28, 2024Produced, Narrated & Written: Isaac ArthurEditor: Ludwig LuskaGraphics:Bryan VersteegFishy TreeJermey JozwikKen York YD VisualNeil BlevinsSegio BoteroUdo SchroeterSelect imagery/video supplied by Getty ImagesMusic Courtesy of Epidemic Sound http://epidemicsound.com/creatorSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
On Episode 201, Eric and Josh are back after a brief hiatus to discuss the history and science of Body Snatching. it's a Halloween Spooktacular! WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
On Episode 199, Eric and Josh discuss the scientific research and current understanding of Seasonal Affective Disorder. How does melatonin play a role? What are the evolutionary factors? Hibernation? WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
PATREON EXCLUSIVE: On Episode 198, Eric and Josh discuss the Science of Stuttering. Genetic research in mice, brain research in songbirds, and the struggles with social stigma. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
On Episode 197, after a brief end of summer respite, Eric and Josh return with harrowing tales of Eric's recent European adventure. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
PATREON EXCLUSIVE On Episode 195, Eric takes us on his own personal music journey! Tracklist: Cosmic Gate - Tomorrow Dragostea Din Tei 2009 (Jumpstyle Remix Edit) - DJ Zone Zombie (Freddy vs. RicoNL Remix Edit) - Andrew Spencer & The Vamprockers Satisfaction (Radio Edit) - Benny Benassi I'm A DJ - Benny Benassi vs David Bowie Woo Boost - Rusko Typhoon (Feat. Chasing Shadows) - Foreign Beggers Scary Monsters and Nice Sprites - Skrillex Gold Dust (Flux Pavilion Remix) - DJ Fresh Bass Cannon - Flux Pavilion Bass Cannon (Getter Remix) - Flux Pavilion WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
On Episode 194, Eric and Josh discuss the science of trying to measure turbulence and the recent breakthroughs being made at the University of Chicago. Plus, donuts at church. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
PATREON EXCLUSIVE: On Episode 193, Eric and Josh discuss finish up the Spielberg trio with "E.T. the Extra-Terrestrial" (1982) directed by Steven Spielberg and written by Melissa Mathison. Starring Dee Wallace, Henry Thomas, Peter Coyote, Robert MacNaughton and Drew Barrymore. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
On Episode 192, Eric takes Josh on a journey through the origins of coffee. From plant biology to the establishment of trade and commodification. PLUS, dog noises. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
Did you know: 91% of mentored employees report higher job satisfaction? And 40% of those without mentor support at their workplace have even considered quitting!Having the right mentor can be invaluable for your career, but it's also very beneficial for mentors themselves and the wider organization. That's why in this episode of HR Unplugged, we dive into the importance of and benefits of building a great mentorship program.Our special guest is Cara Baldwin, Chief Legal Officer at Torus, who brings her expertise on how to create a successful program. We hear why investing in mentorship is time well-spent, how it can help to develop new leaders, and ways it can benefit the wider business. Along with hosts Anita and Vanessa, they explore the worries that organization have about developing people who may leave soon after and why HR isn't the only department that can work on a mentorship program.Key moments: The benefits of investing in a mentorship program The positive impact on leaders who implement a mentor program Why mentors need training to be mentors Addressing the concern that people will leave after being trainedTips on building a program from scratch Advice for refreshing an existing program Key links: Subscribe to HR Unplugged Series: https://www.bamboohr.com/resources/podcasts/hr-unplugged/Join HR Heroes Slack Community: https://join.slack.com/t/hrheroesworkspace/shared_invite/zt-21ad3f1r8-dkWC2EdmyhxUAHw9cGLdQw/Bamboo HR Homepage: https://www.bamboohr.com/Connect with Cara on LinkedIn: https://www.linkedin.com/in/cara-baldwin-43388620/
PATREON EXCLUSIVE: On Episode 191, Eric and Josh discuss more Spielberg! Time for a deep dive into "Close Encounters of the Third Kind" (1977) starring Richard Dreyfuss, Melinda Dillon, Terri Garr, and Francois Truffaut. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
On Episode 190, Eric and Josh discuss Terrence Howard and Eric Weinstein's appearance on The Joe Rogan Experience. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
PATREON EXCLUSIVE: On Episode 189, Eric and Josh discuss one of the all-time great 4th of July movies, "Jaws" (1975) directed by Steven Spielberg, starring Roy Scheider, Richard Dreyfuss, and Robert Shaw. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
O Episode 188, Eric and Josh discuss the recent adventures of Voyager 1, plus Eric has sports questions, middle finger protocol, and the boys are busy throwing shows! WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
It's return guest season here at Latent Space! We last talked to Kanjun in October and Jonathan in May (and December post Databricks acquisition): Imbue and Databricks are back for a rare treat: a double-header interview talking about DBRX from Databricks and Imbue 70B, a new internal LLM that “outperforms GPT-4o” zero-shot on a range of reasoning and coding-related benchmarks and datasets, while using 7x less data than Llama 3 70B.While Imbue, being an agents company rather than a model provider, are not releasing their models today, they are releasing almost everything else: * Cleaned-up and extended versions of 11 of the most popular NLP reasoning benchmarks* An entirely new code-focused reasoning benchmark* A fine-tuned 70B model, built with Meta Llama 3, to identify ambiguity* A new dataset of 450,000 human judgments about ambiguity* Infrastructure scripts for bringing a cluster from bare metal to robust, high performance training* Our cost-aware hyperparameter optimizer, CARBS, which automatically and systematically fine-tunes all hyperparameters to derive optimum performance for models of any sizeAs well as EXTREMELY detailed posts on the infrastructure needs, hyperparameter search, and clean versions of the sorry state of industry standard benchmarks. This means for the FIRST TIME (perhaps since Meta's OPT-175B in 2022?) you have this level of educational detail into the hardware and ML nitty gritty of training extremely large LLMs, and if you are in fact training LLMs of this scale you now have evals, optimizers, scripts, and human data/benchmarks you can use to move the industry forward together with Imbue.We are busy running the sold-out AI Engineer World's Fair today, and so are unable to do our usual quality writeup, however, please enjoy our show notes and the excellent conversation! Thanks also to Kanjun, Ashley, Tom and the rest of team Imbue for setting up this interview behind the scenes.Video podTimestamps* [00:00:00] Introduction and catch up with guests* [00:01:55] Databricks' text to image model release* [00:03:46] Details about the DBRX model* [00:05:26] Imbue's infrastructure, evaluation, and hyperparameter optimizer releases* [00:09:18] Challenges of training foundation models and getting infrastructure to work* [00:12:03] Details of Imbue's cluster setup* [00:18:53] Process of bringing machines online and common failures* [00:22:52] Health checks and monitoring for the cluster* [00:25:06] Typical timelines and team composition for setting up a cluster* [00:27:24] Monitoring GPU utilization and performance* [00:29:39] Open source tools and libraries used* [00:32:33] Reproducibility and portability of cluster setup* [00:35:57] Infrastructure changes needed for different model architectures* [00:40:49] Imbue's focus on text-only models for coding and reasoning* [00:42:26] CARBS hyperparameter tuner and cost-aware optimization* [00:51:01] Emergence and CARBS* [00:53:18] Evaluation datasets and reproducing them with high quality* [00:58:40] Challenges of evaluating on more realistic tasks* [01:06:01] Abstract reasoning benchmarks like ARC* [01:10:13] Long context evaluation and needle-in-a-haystack tasks* [01:13:50] Function calling and tool use evaluation* [01:19:19] Imbue's future plans for coding and reasoning applications* [01:20:14] Databricks' future plans for useful applications and upcoming blog postsTranscriptSWYX [00:00:00]: Welcome to the Latent Space Podcast, another super special edition. Today, we have sort of like a two-header. John Frankel from Mosaic Databricks, or Databricks Mosaic, and Josh Albrecht from MBU. Welcome.JOSH [00:00:12]: Hey, glad to be here.SWYX [00:00:14]: Thank you for having us. Hey, so both of you are kind of past guests. Jonathan, you were actually one of the most popular episodes from last year talking about MPT7B. Remember the days when we trained large models and there was 7B?JONATHAN [00:00:30]: Yeah, back when reproducing LLAMA1-7B was considered a huge accomplishment for the field. Those are the good old days. I miss that.SWYX [00:00:38]: As the things have accelerated a lot. Actually, let's do a quick catch up and Josh, you can chime on in as well. So Databricks got acquired. I talked to you at New York.JONATHAN [00:00:45]: Mosaic got acquired, although sometimes it feels like Mosaic acquired Databricks because, you know, we're having a lot of fun being here. But, you know, yeah.SWYX [00:00:52]: Yeah. I mean, you are chief scientist now of Databricks.JONATHAN [00:00:55]: Chief AI scientist. Careful with the title. As much as I would love to understand how Spark works, I'm going to have to defer that to much smarter people than me.SWYX [00:01:03]: Got it. And I don't know about like what you would highlight so far as a post-acquisition, but the most recent news is that you guys released DBRX. Is that the thing that most people should be aware of?JONATHAN [00:01:13]: Actually, that's no longer the most recent news. Honestly, the most recent news, we announced this, but it was at our Data and AI Summit last week. So it was announced among like 100,000 other things, is that we finally released our text to image model, which has been a year in the making through a collaboration directly with Shutterstock. There was a lot of work put into finding a dataset that we were comfortable with working on and trying to build a model that honestly, I felt like I could trust and that others might be able to trust to put out in the world. So that model was released last week. It's unfortunately just available via API due to the fact that the data is quite sensitive and quite valuable. It's Shutterstock's entire business in a lot of ways, but I'm still really excited that there's now a model that is trained on a dataset where the provenance of every single image is known, and it's a damn good model. So I'm really proud of the team on that.SWYX [00:01:55]: Yeah, amazing. Josh, do you have any thoughts on image model questions?JOSH [00:01:59]: That is not my area of expertise, but I was excited to see the release of it last week as well, and very happy that you guys did a nice job on the data side of everything there. So that was cool to see.SWYX [00:02:09]: I think what's unusual is like, I think Shutterstock's doing multiple deals in multiple labs. So what is the Shutterstock model? Like, I guess, is this the house model for Shutterstock? Is this Databricks' version of the Shutterstock model? Like, what is this?JONATHAN [00:02:22]: The way that I would think about it is that Shutterstock is doing an amazing business in AI across the board. Their dataset is kind of widely known to be the best stock photos dataset in the world, the most comprehensive, the biggest. When you think about like, what dataset am I going to train a multimodal model on? You call Shutterstock. And I, at least I've heard in the news, like OpenAI, Google, Meta, Apple have all called Shutterstock and made those deals. So a lot of models have had Shutterstock data incorporated into them. But this is the only model I know of so far where it was, you know, exclusively and specifically trained just on the vanilla Shutterstock data. There was nothing else mixed in. We didn't go and scrape the web and find other data or combined datasets or anything like that. And so this is, in some sense, the house blend. But the other piece is that it's just a dataset where the provenance of every image is known in public. Where did the data come from? It is the Shutterstock collection. That's it. You know, nothing less, nothing more. And certainly being at Databricks, if I've learned one thing, I've learned about enterprise customers and what they want out of AI. And one of the things they ask for most is just, what can you tell me about the data the model was trained on? And here, especially for text to image models, where images are just tricky subject matter, there's been a lot of kind of legal conversation about images, especially. It's nice to just have something where I can point to it and say, you know, if you want to know where the images came from, these are what they are and this is how they got there.SWYX [00:03:36]: I will talk a little bit about Databricks because it's relevant to the rest of today's episode. So Databricks, sorry, I keep misspeaking. It's DBRX.JONATHAN [00:03:46]: DBRX, actually, there's been a pronunciation update. It is now D-B-Rex. So we have decided to add a dinosaur mascot because what model doesn't like a mascot? So literally, I wish I could pull it up. There is a little plush dinosaur that we had made. It's like the world's cutest dinosaur, but it is the official mascot of D-B-Rex. And there's a little dinosaur logo that, you know, you'll probably see around a little bit more because DBRX is a mouthful, but D-B-Rex, like, you know, it's just kind of...SWYX [00:04:13]: Rolls off the tongue. I love mascots. Like every company should have a mascot. And I think Hugging Face got it right. You need an emoji mascot because that's the minimal viable image.JONATHAN [00:04:21]: I probably shouldn't talk at all about, you know, Velociraptor, but, you know, that's a, maybe that's something we can talk about later in the summer. I'll just leave it at that.SWYX [00:04:28]: Okay. That's a hint to names. I feel like your names leak a lot of alpha. So just to quickly cover the headline details, DBRX, as Make Sure Experts model, that's fairly big, 132 billion total parameters, so 36 billion active on any input, pre-trained on 12 trillion tokens of text and code, and did really well on evals to the point where you had to dye your hair blue. That's my high level conclusion.JONATHAN [00:04:53]: Never make a bet with your team two weeks out from model launch, even when, you know, human eval is looking quite bad. Because if you set some bar, even if it's arbitrary and you think there's no way in hell they're going to hit it, apparently money doesn't motivate people anymore. Humiliating their boss motivates people. So Josh, you should really take a hint from this. You know, you cannot pay someone enough money to make up for you dyeing your hair blue.JOSH [00:05:15]: I'll keep that in mind for our next model.SWYX [00:05:17]: It works. So speaking of Imbue's next model, perhaps Josh, you want to actually just say hi to the general sort of latent space audience and talk about what we're releasing today. Yeah.JOSH [00:05:26]: I'm Josh, CTO of Imbue, and we're not releasing the model. We're not releasing the weights, but we are releasing a bunch of different things that should make it easier for other people to make their own models. So I think right now, training foundation models from scratch is like a very difficult, time-consuming, expensive, kind of risky endeavor, especially for smaller companies. And the things that we're releasing hopefully make that at least a little bit easier. So the things that we're releasing fall into kind of three different buckets. One is infrastructure and scripts for dealing with the kind of hardware and hardware failures and understanding how well is the actually lowest level of thing actually working so that you can actually do your training at all and at a reasonable speed without having to constantly restart, etc. So infrastructure and training scripts. A second set of things is around the evaluation. So after you've trained it, like how well is this actually working and how do you know how well it's working? We're releasing a whole bunch of different data there, a new benchmark about code, reasoning, understanding, as well as our own private versions of 11 different open source benchmarks. So things like pool queue or ANLI, where we've gone through and kind of cleaned up the data as much as possible by looking at all the ones that models get wrong or that are flagged for ambiguity and also our own kind of private reproductions of those where we've done like a kind of clean room black box, like, okay, this is what the data set is supposed to be. Here are some examples. Let's make our own version of this to make sure that there is no data contamination, etc. To make sure that we're actually, you know, not testing on train. And then I think a final thing that we're releasing there is around 450,000 human judgments about ambiguity and question quality, which we used in the process of cleaning these evaluations and we also hope will be helpful for other people training kind of similar models. And then the third thing is CARBS, our hyperparameter, our cost-aware hyperparameter optimizer, which was especially helpful for being able to experiment at much smaller scales and then scale those experiments up to the much larger scale kind of on the first try without having to retry it. You don't want to be training, you know, 10, 20 different 70B models. You really want to get these larger modelsSWYX [00:07:30]: right on the first try.JOSH [00:07:30]: And so the ability to kind of tune things very precisely and learn scaling laws, not just for, you know, the like data and flops, but also for learning rate and all the other hyperparameters and see like how should you scale these things up was extremely valuable to us as we were training the larger models. Yeah, that's a lot of stuff.SWYX [00:07:49]: Yeah, exactly. So there's a bunch of stuffJOSH [00:07:50]: we'll have to go through all of it.JONATHAN [00:07:52]: Yeah, I just want to throw in how excited I am about this. This is the stuff that nobody ever talks about. That is the difference between success and failure in this stuff. Like, can you get your cluster to run? Can you get software on your cluster? Can you figure out what broke? Because fault tolerance is still not really built into any of the fundamental primitives of training models. And so if something breaks, you have to go figure out what broke, your job stops, you have to restart your job. It is a nightmare just to get to the point where anything can train on the cluster. A basic MPI hello world that has the GPUs talk to each other is hard enough, let alone actually training a model, let alone getting good performance out of the GPUs, let alone actually getting a model that converges to anything interesting. There's so many levels of things you have to accomplish. This is the kind of stuff that matters. I think to a point that Josh made earlier, before we got on here, there are plenty of weights out there. Nobody's released this.JOSH [00:08:46]: Yeah, that was part of the motivation actually is that there are lots of other things that are complimentary, but I have not seen nearly as much discussion about some of these other things that we think are pretty important. I mean, in some sense,SWYX [00:08:56]: I'm very excited to have Jonathan on because this is a little bit, you're a bread and butter with Mosaic. And I think you've released some part with Composer. And I think it's just really interesting to see like a different take, basically a full stack take that's kind of open source today.JONATHAN [00:09:18]: Yeah, it's really kind of, it's been an ordeal to figure this out. And every time something changes, whether it's a new GPU or even a new driver update, you get new creative errors and new things go wrong. And, you know, we've dealt with the weirdest things from, you know, our InfiniBand cables getting stolen from the data center twice, like in boxes before they arrived at the data center. Like, you know, Porch Pirate basically had stolen our InfiniBand cables back when those were hard to come by. To like, you know, weird recalls of switches to like the strangest stuff has happened. I have my favorite GPU failures I've seen, like ones where the GPU doesn't fail, it has a correctable memory issue and the memory correction causes the GPU to become a straggler and hold up the whole job. Like weird stuff happens and figuring out how to not just identify all of that, but then eventually productize it, is in some sense, the entire story of Mosaic and now Databricks in terms of our ML offering. Really, the thing we offer is we have gone through this suffering and figured out how to even productize that. It has been a pain in the butt.SWYX [00:10:20]: Yeah, it's a lot of work.JOSH [00:10:20]: I think my favorite failure was GPU is just giving wrong math. Like if they give errors, great, because you can see the errors, but if they just give you the wrong math back, not so fun.SWYX [00:10:30]: When did they give you wrong math?JOSH [00:10:32]: Like literally you could just, you know, add two things. For example, the numbers come back. They're not the numbers that they're supposed to be.JONATHAN [00:10:40]: I think it's important to say at this stage, just because like it, I think it goes without saying for Josh and I, but it's worth saying here, this isn't to say that like anything is wrong with us. It's not like NVIDIA did a bad job or, you know, Mellanox did a bad job or the like the server builder, the data center operator, the cloud provider, like the million other parties that are involved in building this. We are running these insane chips that are huge and complicated and built on tiny transistors at insane frequencies with insane heat in data centers that for the most part, were not built remotely for this kind of power or heat and have been retrofitted for this. Like failures happen on a good day with normal CPUs. And this is not a good day and not a normal CPU for the most part. It's fun to joke about all the weird things we see. This is not to say anybody's done anything wrong. This is just kind of part and parcel of working on a massive cluster running at multiple megawatts of power at a time.SWYX [00:11:32]: It's crazy. Yeah.JONATHAN [00:11:33]: So optical cables, like all sorts, like everything.SWYX [00:11:37]: I'll take the opportunity to start going to the sort of infra piece. There's just like a description of the infra just to give people a sense of what we talk about when we talk about massive clusters. So I'm just going to read off the blog post here. This post is about one cluster that has 4,092 H100 GPUs spread across 511 computers. They use unified fabric manager nodes, which manage the infinite band network. And you talk a little bit about your networking. Is there anything unusual about this setup that you'll call out to people?JOSH [00:12:03]: Yeah, actually this particular cluster is a little bit non-standard. The normal, like vanilla setup for these large clusters as vanilla as it can be is what's normally like a 127 node cluster. So closer to like 1024 GPUs instead of 4,000. Here we have a larger cluster. As you start to get into the larger clusters, the networking becomes a little bit more custom. It's a little bit more, it's a little bit trickier. It's a little bit more difficult to get these things to all be able to talk to each other at the same speed. And so this has, in this particular case, this is a three tier network architecture instead of two tiers, kind of the normal one. So most of the clusters are a little bit smaller. As you get to even larger scales, then this becomes even much more complicated,SWYX [00:12:43]: much more expensive.JOSH [00:12:43]: So we chose this particular scale, kind of knowing our own workloads and kind of what we wanted to do. This was kind of the right size for us. But yeah, I think it's not exactly vanilla already. It's already getting into kind of the custom territory.SWYX [00:12:54]: So my understanding is that there, and is there any part of this that comes with the Voltage Park deal that you guys had? Is that part of the hardware that you got from the deal with them?JOSH [00:13:04]: Yeah, so we worked really closely with Voltage Park to set up all their clusters and infrastructure and everything and kind of decide even like what to order, how should the networking work? Like we were very involved in kind of the construction and bring up of this. And that's what this post is about, is about that process of like bringing up all these, there's like different clusters in different places of different scales. So in this particular post, we're talking about this one 4096 GPU, but there are other clusters that they have as well. And we were very closely involved with figuring out the exact architecture and kind of the trade-offs that go along with picking, you know, those exact components. You really don't want to like place the wrong order because it takes months to get it and it's very expensive. So yeah, we were happy to help out with that.JONATHAN [00:13:43]: And then your bit of good cables get stolen.SWYX [00:13:44]: Yeah, yeah, exactly.JOSH [00:13:47]: We wanted to make sure that we ended up with compute that would work for us and that would also work for their other customers. And so we kind of helped design something so that we would get exactly what we were looking for. We knew that these kinds of details would be super important and that getting down to the level of the hardware and like having these good scripts and everything was going to be a core part of like actually getting this to work. I'm very glad that we did that. I don't think that most companies kind of take that full stack approach, but for us, it certainly paid off.SWYX [00:14:12]: Yeah, it's basically sort of built to spec. It's interesting that relationship because you usually, for the rest of us who don't operate at your scale, we take whatever we can get from cloud providers, but you are basically co-designing from the single machine up. And you described that a little bit. Do you want to take us through the process that you described here?JOSH [00:14:27]: Yeah, so for the actual, like the blog post and kind of bringing these machines online.SWYX [00:14:32]: Yeah.JOSH [00:14:32]: So yeah, I think the process, as we have it broken down in the blog post, there's kind of a few different layers. First is like getting the individual machines to work at all and then getting the machines to actually be able to talk to each other. So getting the InfiniBand networking to work and then getting to a point where, you know, not just the machines are working and they can talk to each other, but everything is actually working correctly. There's a big gap between like it's working at all to it's working perfectly correctly. And then after you have all this stuff working perfectly correctly, nice and healthy, then now you get into kind of the software data, like training issues. And then after that, you're still not done. Like now, even once you're training at full speed, things are going to fail over time. Things are going to change. There's going to be new, you know, firmware updates. Like how do you kind of deal with this change and flux over time without going crazySWYX [00:15:16]: and pulling your hair out,JOSH [00:15:16]: trying to like reproduce things or understand why there were regressions. And so there's a lot of work to kind of automate the infrastructure tooling as well. And kind of the first step, like bringing these things online in the first place, you know, you have hundreds of machines at this point. So you don't necessarily want to be like walking around with like a CD-ROM or a USB drive, like plugging it in with your keyboard, like hitting next, next, next on the OS install. That's not how this works. You do that for one machine. And then you use, we use this thing called Metal as a Service to bring up all the other machines. So it's a kind of server that can kind of install the operating system on these other machines. So most like when you're talking about these machines, like each machine is, you know, on the order of hundreds of thousands of dollars. So they usually come with a kind of out-of-band management interface as well. So they don't, they have their InfiniBand networking. They have their normal 100 gigabit per second Ethernet networking. These are like dual, redundant, et cetera. And then you also have this extra out-of-band management network. So you can log in and you can see like the boot screen or you can see the blue screen of death. You can like get in there and actually see what was wrong, which is pretty fun. And it makes it like possible to automate a lot of this work. So the beginning of that, and the blog post goes into much more detail about like exactly how we set these up and kind of the other errors that we ran into. When you're bringing these online, you'll definitely have failures. Even if they all worked in the factory, they get shipped, some parts come loose, something fails, something goes wrong. So when you're bringing them online, there'll be some that don't quite work for all sorts of reasons. As you start to be working with machines at this scale, like if something happens one in a thousand times, you're like pretty likely to see it. And so you can get pretty rare, weird things, especially since we had fairly early builds and fairly early versions of this hardware. Like these are some of the like first machines that were ever produced, some of the first GPUs. So you've got some extra special things there. We definitely worked with Dell, for example, on making fixes in the firmware level to be like, okay, like this thing is wrong. Like we need to update this at the firmware to like actually fix this particular thing. So we worked pretty closely with Dell and Nvidia. Yeah, that's what I'm saying. Like this stuff gets complicated. And the thing is like, you know, taking a step back, the whole reason we're doing this, right, is that we knew that this was going to be complicated. There would be these kinds of failures. And if we're just using, you know, AWS or some other cloud provider, these errors are still gonna be there and you're gonna have no way to know and no way to debug this and no way to diagnose what's going wrong. And so we would much rather be able to like call up Dell and say, hey, this isn't working. And they're like, yep, okay, cool. Let's debug it together. Oh, I see. Yeah, cool. We'll ship a firmware update and actually fix this for you. That was a much better experience than like, great, just magically fails. I guess we restart and hope that that machine goes away. Like that's not a very good place to be. So yeah, that's kind of the first place is getting to a place where like GPU training is working on your single node machines. You can observe stuff. We have tons of tooling around like, you know, Prometheus and all sorts of other tools for understanding what's going on in these machines because you don't want to be like logging into each one and looking at the temperature or something you really need to have tooling to collect all these metrics, et cetera. Unfortunately, all of the scripts that we have for this are like for this entire cluster and for all this infrastructure are a little bit like special purpose for our particular thing. So it's not that every script that we have, it's not that you can just like take this and plug this in. Even if we did open source all the tooling that we have, you'd still have to do like a lot of work to open source it. What we are releasing is as many of the things that we can that are going to be useful for other people. You're still going to have to have some way of kind of managing these things, making your own like logging aggregators, et cetera, et cetera. So that's kind of bringing them up to the like, you know, the single nodes that are working. From there, it goes into, I'm happy to keep going if you want. Well, I just want to leave the opportunity for JohnSWYX [00:18:53]: to comment if there's anything that's different from how he runs things.JONATHAN [00:18:57]: Oh, I mean, all I'll say is I'll endorse this and say this s**t is hard. Like this is really, really hard. And, you know, I have a special props to, you know, the folks in Vue because they were building this from the ground up. You know, at Databricks and at Mosaic, we typically work with cloud providers because some of this stuff is just, there's too much to handle. It's complicated. There's a lot to deal with. And this doesn't even get into things like physical security, you know, securing power if you're the data center operator. Like this gets infinitely complicated and you have to abstract somewhere. Like, you know, and then you get to the folks who are literally building their own custom chips and like, good God.SWYX [00:19:36]: Like, oh my God, that's, you know,JONATHAN [00:19:38]: if you're one of those folks, you're having, you know, pour one out for the infra people at some of the AI chip startups who are having a really, really interesting time right now. But this stuff is really hard. And I don't think we talk about it much because there's so many other things that are hard. But the other hard things, I think everybody's becoming pretty familiar with at this point. This is something that I don't think there's ever really been a comprehensive discussion of, at least not that I've seen.SWYX [00:20:00]: Yeah, so my impression is that you guys, Mosaic, have your own software for sort of spinning up and down machines, just like Imbue had to build. But Imbue probably, it sounds like Imbue, you guys went fuller stack. I don't know how to describe it. Like Mosaic is not working with Dell on like their firmware.JONATHAN [00:20:21]: No, no, we're typically working with like, you know, pick your cloud provider on their Dell firmware or what have you. Like, it's kind of, I think one of the things, I don't know, Josh, you can correct me on this. It's kind of impossible if you're doing training to not go all the way through the entire stack, regardless of what happens. Like somehow I'm still chatting with cloud providers about power contracts, even though the whole point of dealing with the cloud provider is not to have to think about power contracts. Somehow I'm still asking them about which InfiniBand provider they used this time to see if this is part of the bad batch of cables I encountered on that cloud provider or what have you. Or like, we're still talking about a firmware update from pick your provider. You can't not do this. It's convenient that they have data center staff who are worrying about what to send back to which provider when, and they have people who can go and wait for the InfiniBand cables so they don't get stolen outside. But, you know, it's kind of, it's impossible not to really go full stack if you're thinking about the infrastructure at all. I don't know, Josh, correct me. No, I think that's right.JOSH [00:21:17]: That's what we expected from the beginning as well, is that we would inevitably have to get into the details here. And I'm glad that we kind of just planned for it. I think it made it a lot easier from our perspective to have direct control over this. Instead of having to go to the cloud provider that goes to the data center, that goes to the supplier, we could just go direct to NVIDIA or DellSWYX [00:21:37]: or the data center,JOSH [00:21:37]: whoever was responsible and be like, hey, this thing needs to change. And they're like, oh, okay. Yeah, that is our responsibility. Great, we can fix that. So it was just a lot easier for us to fix these bugs than if we had to go through an extra layer of email.SWYX [00:21:48]: Something we discussed in the pre-show was that you had a rule of thumb for your cluster of reliability. You say here in the post, by and large, you expect around 3% of your machines to break every week. So you're basically going to turn through all your machines in a year.JOSH [00:22:04]: As it says in the post. So that would be true if it was a uniform failure like that. But as it says in the post, it's usually these kind of problematic nodes. And to be clear, that is the number that we've heard from other people is like they're having about 3%. I don't think we're experiencing failure rates that are that high. I think ours is actually quite a bit lower than that, probably because we've taken the time to like dig into a large, maybe larger number than we should have of these failures and get to the root cause of it and be like, oh, okay, like that's exactly what's going wrong.SWYX [00:22:33]: How do we fix this?JOSH [00:22:33]: How do we prevent this from happening? How do we make automated checks for this so that if it does happen, it just goes back to whoever owns that particular part of the process and they can fix it immediately.SWYX [00:22:43]: And that's part of what you're also open sourcing, which is the health checks, right? You got the NIC health checks, GPU health check, this space health check, Docker D message. I don't know what that is.JOSH [00:22:52]: That one is just a lot of stuff.SWYX [00:22:54]: Yeah.JOSH [00:22:55]: That one is one where we realized that actually like when these machines boot, sometimes they wouldn't actually boot cleanly all the way. Or when they rebooted, they had problems that they didn't have when they were working before, which was kind of frustrating. Like usually if you restart your computer,SWYX [00:23:08]: it gets better.JOSH [00:23:08]: Here you restart. It did not get better.SWYX [00:23:10]: It got worse.JOSH [00:23:10]: That was very frustrating. So this health check looks at every particular line we've ever seen from the boot, like in D message, like every single log line that your computer emitsSWYX [00:23:21]: and says like,JOSH [00:23:21]: have we ever seen this before?SWYX [00:23:23]: Is this expected?JOSH [00:23:23]: Is this in the right order? Or is there something out of place? If there's anything out of place, let me say, okay, great. Like now it goes into this, like longer, more triage list of like, all right, great. Like, is this acceptable?SWYX [00:23:33]: Should we flag this?JOSH [00:23:33]: Like, should someone take a look at this? So we're looking down at a very, very granular detail level, what's happening on these computers to make sure that nothing is out of place. And that's critical because without that, if you're running your training, as Jonathan said, and this thing is slow, like what are you supposed to do? Right?SWYX [00:23:49]: Like you really,JOSH [00:23:49]: you really want to be very certain that like all 4,000 of these GPUs are working like they're supposed to.SWYX [00:23:54]: We know that.JOSH [00:23:54]: And so if it's slow, it's because like we messed up the config or something else and not because of this earlier thing that's like really hard to detect in software later.JONATHAN [00:24:01]: Yeah. I think the, I'm just curious to ask,SWYX [00:24:03]: like, you know,JONATHAN [00:24:03]: suppose you were to set up another, let's say another H100 cluster and it were at a different data center. And instead of the vendor being Dell, it was super micro or what have you. How much of this would be repeatable? And how much of this would you have to redo? I, you know, I genuinely don't know.SWYX [00:24:18]: A decent amount.JOSH [00:24:19]: I think it would go a lot faster the second time. I think there's lots of learnings that we had. And also the blog post,SWYX [00:24:24]: you know, yes,JOSH [00:24:24]: we are releasing the health checks, releasing some scripts, but a lot of the valuable stuff is also in the blog post itself, in the details and kind of the, you know, the learnings that we've had and the sort of errors that we run into. We tried to as much as possible surface those to other peopleSWYX [00:24:36]: could learn from thoseJOSH [00:24:36]: and avoid the same mistakes or failures as well. But I think it would go a lot faster.SWYX [00:24:41]: Although, yes,JOSH [00:24:41]: there would certainly be some things that'd be a little bit different. I mean, there'd probably be different CPUsSWYX [00:24:46]: or whatever,JOSH [00:24:46]: but I think a lot of that stuff is less,SWYX [00:24:49]: it's less,JOSH [00:24:49]: that's the like, that's less variable. I think most of it would apply the second time around. Although I'm sure next timeSWYX [00:24:56]: we're building one,JOSH [00:24:56]: it'll probably be, you know, at a scale that's 10x as big with a different chip or something like this.SWYX [00:25:00]: And then who knows?JOSH [00:25:01]: Yeah, with Kinect X8,JONATHAN [00:25:02]: that will have its own fun behavior and all that good stuff. Yeah.SWYX [00:25:06]: Perhaps there's something that people don't discuss about, and you don't even talk about this in the blog, but I always wonder is what is the timeline that's like kind of reasonable for this amount of work, at least the initial stages? And also what does the team composition look like for setting up a cluster, right? Like what are the mix of skills that you typically would require to get all this going?JOSH [00:25:27]: I'm, I can't really speak to typical. One thing I am very proud of is how much we accomplished with such a ridiculously small team. Like our infrastructure team is like, you know, fluctuates from week to week, depending on like how many things are on fire and how much we need to build. But it's like between like three and six people, like it's small. It's not like some huge team of like tons and tons of engineers. But those people are very, very good at what they do. And so that has allowed us to get a lot of mileage out of out of these things. I think it's not that we're building everything, right? It's not that three to six people build this whole thing. I definitely want to like, you know, say thanks very much to Dell and H5 and NVIDIA and the other people that have done a lot of the work, like to bring up this cluster, you know, with 4000 GPUs and three tier networking, networking architecture, you have 12,000 cables. So that's 24,000 things that need to be plugged in. Like that's just a lot of stuff to plug in, right? And you don't want to mess it up. Like each one needs to be done correctly. Like it's a little bit loose. Like it doesn't really work.SWYX [00:26:23]: If you break it,JOSH [00:26:23]: you need to replace it. Like there's a lot of workSWYX [00:26:26]: that goes into this.JOSH [00:26:27]: Yeah.SWYX [00:26:28]: And then, you know,JOSH [00:26:28]: that's just like that's it. That's if you were to do everything right the first time.SWYX [00:26:32]: And if you didn'tJOSH [00:26:32]: have to fix anything. But inevitably, you know, you will have to replace something, which means like taking all the wires out, pulling the thing out, taking all the GPUs out, going and fixing some cable, putting it all back correctly, putting it back in, doing this every time. So there were a lot of people at Dell, NVIDIA and at H5 that all helped a ton with this stuff. I don't know the exact size of the Dell team. It also fluctuated over time.SWYX [00:26:55]: Yeah, excellent. And then, you know, you so you have all the hardware set up and now you're firing it up for a single node. There's a long description that you guys have about just like monitoring the MFU, right? And what each situation might look might be indicative of. One of the most interesting things to me that I saw from here is like, you know, if training immediately starts off at 60 to 80% MFU, something's wrong.SWYX [00:27:24]: But like, you know, like what what are like, you know, some anecdotes or, you know, notable scenarios here that you might you might call out as maybe counterintuitive or super interesting.JOSH [00:27:36]: There's just so many of them. I mean, one of them, which I think is probably pretty common, like common knowledge by this point. But like we did have a sort of likeSWYX [00:27:46]: which one was this exactly?JOSH [00:27:47]: I think for the MFU, like gradually getting worse over time. I think that one, when we saw that the first time we were like, what the heck is going on? Like, why does it get just like a little bit worse? This is so strange. Like, what is it getting lazy or tired or something? Like, is it heat? Like what's going on? And in this particular case, it was memory fragmentation. Because you have hundreds of machines, they're doing garbage collection slightly different times. And then they get slightly further apart and slightly more and more jittered until eventually they're all happening kind of at random times. And just like really messing up each one of your steps. So you just turn off garbage collection and call it a day, basically,SWYX [00:28:20]: to be honest.JOSH [00:28:20]: There's other things you can do if you want to be a little bit more sophisticated about it. But you can also just manuallyJONATHAN [00:28:25]: have it all garbage collect on some interval. Like that's what we've done. We just have a garbage collection callback that just runs. But I've seen the exact same thing.JOSH [00:28:33]: Yeah, yeah, exactly. So I thought that one was kind of funny. And we did trace that one down and look and we did find the actual call. Like, again, this goes to like having good tools. So we had really good tools where we could look at a bunch of like actual traces in C and be like, OK, cool. This is the thing that's taking a lot of time. Or like, you know, this is the thing that doesn't quite line up here. Like, oh, I guess it's garbage collection. OK, cool.SWYX [00:28:52]: Interesting.JOSH [00:28:52]: Yeah, let's just try taking it off.SWYX [00:28:54]: OK, great.JOSH [00:28:54]: That's what it was. Now we can fix it. So for each of them, like basically bugs are not hard if you have good tools. But if you don't have good tools, bugs can be very, very hard. So similarly for like heat, another thing that we saw was like, oh, you know, the CPU is getting throttled. OK, well, it's easy to see if you're monitoring the CPU throttling or monitoring the heat. If you're not monitoring that, it's really hard to know why it's just suddenly one of them is going slower. I noticed also in the pieceSWYX [00:29:17]: that you mentioned FSDP with 0.3. Actually, we met, I went to iClear and Guanhua from the DSP team was there presenting 0++. I was wondering if you want to make any call outs to, you know, particular open source or open library or open whatever implementation teams that were super helpful in your process. I think we ended up actuallyJOSH [00:29:39]: pulling from a whole bunch of different ones to pull things in into our own particular pipeline. So we use things from NVIDIA's, you know, Megatron stuff. We use stuff from probably DeepSpeed. I think we pulled in a bunch of different pieces from a bunch of different places. So it was really nice to see all these working open source like examples. I think I really appreciate all the effort that has gone into actually tuning these things because you can tune them, but it's a lot of work to like tune this stuff and do all this stuff from scratch. It's really nice to have like a working example. I think those are probably the two biggest ones, DeepSpeed and Megatron alone, but there are probably other ones as well.SWYX [00:30:13]: Is there a particular thing in the ecosystem where you would call out as like, you know, there should be something here that is open source, but like it's not really, it's like everyone kind of builds it on their own. I want to say something with the file system because everyone talks about the file system eventually.JOSH [00:30:28]: The file system actually was,SWYX [00:30:30]: I mean, we did somethingJOSH [00:30:31]: kind of dumb there. Like we have our own sort of local mirror so that we can, you know, like a crappy version of S3SWYX [00:30:38]: that's local,JOSH [00:30:38]: but it's just a pretty simple script, right?SWYX [00:30:41]: Like I think we run likeJOSH [00:30:41]: a little web server that just like serves files and then, you know, it can upload themSWYX [00:30:45]: and download them.JOSH [00:30:45]: Okay, great. And part of the reason we did that is that our internet connectionSWYX [00:30:50]: in the beginningJOSH [00:30:50]: was not the like full speedSWYX [00:30:52]: one that we wouldJOSH [00:30:52]: eventually have. And so we are a little bit more kind of bottlenecked in terms of internet bandwidth. And so we had this. I think we looked at a bunch of services out there like Minio and some other ones, but a lot of these like come with a lot of extra overhead and maintenance. And since we already have so much infrastructureSWYX [00:31:09]: to deal with,JOSH [00:31:09]: we kind of didn't want to, you know, bring in a whole other like cloud provider, virtualize something, something.SWYX [00:31:14]: We just wanted something simple.JOSH [00:31:14]: So we went with that, which has been quite helpful. Like our toolsSWYX [00:31:19]: are usually quite simple.JOSH [00:31:19]: It's like Bash and Python and SSH and Docker. Like we'd like to keep things simple so that's easier to debug, like less layers of infrastructure, less layers of abstraction, make it a lot easier to work with. Like we don't use Kubernetes,SWYX [00:31:30]: for example,JOSH [00:31:30]: and we just directly launch these things. And it's just been much easier to debug this way. One tool actually that does come into mind that I will call out is Kraken from Uber. That was great. We love that tool. We were a little bit skeptical. What is it?SWYX [00:31:44]: I'm sorry. Yeah.JOSH [00:31:45]: So Kraken is this, yeah, it's a distributed like Docker registry, basically, that uses BitTorrent to like transfer things between the machines in a sort of nice optimal way. Like in the very beginning, the naive way is like you have this one Docker registry, which was outside of the cluster. So every time we change an image, you know, there's many gigabytes that each of the 500 machines needs to download.SWYX [00:32:07]: So that just takesJOSH [00:32:07]: a really long time. So what this thing does is like just one of them downloads it and then like they all sort of broadcast all the pieces to each other. And it was just like a really nice, fast way of getting these images down. And it was very robust.SWYX [00:32:19]: Like there's a lotJOSH [00:32:19]: going on under the hood, but I think it's a pretty cool tool that we haven't really had any bugs with it at all. Amazing.SWYX [00:32:26]: Yeah. I mean, that's all my questions, I guess, for the info piece. I don't know if, John, you had something that you were sort of burning to ask or.JONATHAN [00:32:33]: No, all I can say is just sameSWYX [00:32:36]: in a lot of places, like, you know, and they're done thatJONATHAN [00:32:38]: seeing this plus one. I think the one big difference, you know, perhaps in philosophies is we've tried to basically standardize on as much commodity stuff as possible, just because, you know, I think the reason I asked about trying to do thisSWYX [00:32:50]: on multiple differentJONATHAN [00:32:50]: pieces of infrastructure is like, I think we're running on like six or seven different clouds right now. And everybody has done something slightly different. And my gosh, the little differences add up as you know, you've seen. And so, you know,SWYX [00:33:04]: our philosophy has been like, whatever the hellJONATHAN [00:33:05]: we can standardize, please let's standardize it. Like vanilla off the shelf FSDB.SWYX [00:33:10]: And like, you know,JONATHAN [00:33:10]: we wrote our own data loader, but we've tried to make that as much of a standard as we can across our infrastructure and in Databricks, because things just start getting really complicatedSWYX [00:33:18]: or like we useJONATHAN [00:33:18]: Kubernetes extensively because it at least gives us a uniform set of APIs. Like that's our hardware abstraction layer to a certain extent for everything else. So it's just, you know, a difference in philosophy there. But otherwise, like, yeah, this stuff is really, really hard. And I feel like we take for granted how much of this, you know, is done for us when you go and you just query chat GPT, for example. Like, oh my God, everything going on underneath that, you know, it's kind of a miracle that the machines boot up, let alone that you can like query a giant language model that's probably doing inference across multiple machines and was trained across thousands of machines. Like, you know, minor miracle.SWYX [00:33:54]: Yeah, it is an awesome amount of power that we invoke with a single API call that we take for granted these days. It's absurd. Yeah, I mean, like Kubernetes, like that point about Kubernetes, I will say as a former AWS employee, like it seems like it would be ideal for imbue to at some point make it more abstracted or agnostic because you're going to want to, you know, replicate your setup. We do have our ownJOSH [00:34:19]: sort of replacement. It's just a much simpler version of Kubernetes. Kubernetes is really designed for running services, not for running experiments. Like that's not its like main architecture. And so for us, like we have everything that's like, cool, you're going to run an experiment. So you want it to run to completion, right?SWYX [00:34:34]: OK, great.JOSH [00:34:34]: Like the primitives are sort of built around a slightly different style. And that makes it a lot easier, like just a lot simpler to fit that the nature of like these machines are going to disappear. They will need to be rebooted for infrastructure upgrades. They will like something will happen to the GPUs. Failure is like baked into this as like a core part of our infrastructure. So it's not that we don't have an abstraction. It's that it's a sort of simpler, more tailored abstraction for the particular work that we're doing.JONATHAN [00:34:58]: Yeah, I think it all depends on what your goals are. And like, I think the challenge in a lot of the deep learning stuff right now is that people are trying to like, people often build things that are more complicated than necessary to get the job done. And the complication is the enemy of everything. You know, don't use a fancier parallelism strategy than you have to. Don't use a fancier set of libraries than you have to.SWYX [00:35:18]: Don't do anythingJONATHAN [00:35:18]: that you don't have to do because it's hard enough as it is. Like, don't overcomplicateSWYX [00:35:23]: your own life.JONATHAN [00:35:23]: Don't try to bring in more tools or more fancy architecture tweaks if you absolutely don't have to.SWYX [00:35:29]: Like getting to the minimumJONATHAN [00:35:30]: necessary to get the job done. And it's really tempting to want to try to use everything. So like, I totally understand that one.SWYX [00:35:37]: I think the last piece I'll maybe call out is that I'm just going to weave this in just because I see the opportunity to do it. Are there any infrastructure shifts that need to be, that need to rise because of changing architecture? So I think, for example,SWYX [00:35:57]: you're announcing a dense model, a 70B dense model, whereas John just worked on DBRX and the image-to-text model, which presumably has different bottlenecks.JONATHAN [00:36:10]: That's correct for us. You know, we train both dense and mixture of expert models. The one we happened to, you know, kind of get permission to open source was a mixture of expert model. And those models are very demanding when it comes to network bandwidth, at least if you're training them in kind of FSTP 03 style, where there's just a lot of parameters getting shuffled back and forth. And your ratio of kind of compute to amount of data that you have to shuffle back and forth becomes a lot worse because you're now, you know, you're only using a fraction of the parameters for every token instead of all the parameters. And so we had to really push the envelope on getting all the stuff to the right places on time. And so actually the networking part of DBRX was the single hardest thing, I think, of the entire process. Just get MOE training, working at scale across a big cluster. We still managed to, I think, do it all with commodity parts, which was very exciting. You know, we were using FSTP and we eventually used HSTP so that we could have HSTP as a version of FSTP where you have multiple smaller replicas and you're doing data parallel within those replicas. And that helped a lot with network latency issues that we were running into just because we were transmitting so much data, you know, for every single part of the process. I think it actually, like, it was instructive for how Google designs their hardware and software together personally. Their training, as far as I understand, using kind of a 03 style of training and have been for a while. They also train mixture of expert models. TPUs have a very different network bandwidth to compute ratio. They have a lot more bandwidth just objectively. And TPUs per chip tend to be a little bit less compute intensive and have a little bit less memory. You know, it's just a different design choice. So the ratio of flops to bandwidth is very different. And that means that it's much easier for Google to be able to pull offSWYX [00:37:54]: some of this stuff.JONATHAN [00:37:54]: They also have interesting, you know, Torus style network architecture or Torus style, like, literal network architectureSWYX [00:38:00]: is not like the model,JONATHAN [00:38:00]: but the network.SWYX [00:38:02]: Is this the sort of block attention? I forgot what you call it. So this is just more or the,JONATHAN [00:38:07]: yeah, this is more, not the ring attention, but these are the ring all reduces. Like you have three different dimensions of rings because they kind of put you in these three dimensional Toruses from what I understand. And so like, you know, Google's infrastructure in some sense is kind of, I wouldn't say built for this, but maybe the way that Google trains models is built for a slightly different bit of infrastructure they have. And it's kind of neat to think about that. You know, as one thing that I think NVIDIA announced for, you know, for, for both the GH200 and the GB200 is this hybrid networking where you'll have blocks of NVLink network chips. I think for the GB200, I think it's like groups of 72 GPUs will all have NVLink to each other. So higher bandwidth, then you'll have normal networking of some kind, InfiniBand or Rocky or what have you between these blocks. And that's kind of a, you know, it's a change due to the fact that, you know, it's hard to build really high bandwidth networks over very large groups, but it is now a blocked networking. And you have to think about how you architect your model and your parallelism differently. You also have to think about fault tolerance differently because it now matters where you lose a GPU, whereas it didn't before. So, you know, it's, it's, it's just all really interesting and really fun speaking personally, but it's going to mean new nightmares when we all move to that generation and have to think about, you know, new versions of these problems.JOSH [00:39:20]: As you go up to larger scales, it gets quite different. Like right now, you know, if you're experiencing, let's say, for example, you experience a GPU failure every day, that's fine.SWYX [00:39:31]: Just restart.JOSH [00:39:31]: If you make your thing 24 times as big, now it's once an hour. Now it stops being quite as easy to just restart, right? So now you have to kind of break, like bake in this sort of redundancy that you didn't have before. So I think as you go up in scale, you end up running into like a lot of really interesting problems that also inform the, the actual like design. Yeah, I mean, as an orchestration guy,SWYX [00:39:52]: this is why I always emphasize like very cheap storage or very fast storage. So you can checkpoint more, but I don't think that's probably not the best solution to for fast, you know, training.JONATHAN [00:40:05]: Which works fine when you're doing language and then you move to vision or video. And then, you know, you have multi petabyte datasetsSWYX [00:40:12]: and getting, you know,JONATHAN [00:40:13]: cheap, fast multi petabyte storage starts to bite. Like I've certainly encountered issues where the literal data center where my GPUs were did not have enough, you know, object store to fit the datasets that people wanted to bring into that data center from whichever users were, were trying to bring them in. And then you get to a wholeSWYX [00:40:31]: different world of hurtJONATHAN [00:40:31]: where you have to keep your data in a different region because the region is just out of storage. So things get fun really fast.SWYX [00:40:39]: Speaking of vision, Josh, actually, you know, Embu is an agents company, but you're only, you're announcing a text-only model. What, where does, where does the vision side come in?JOSH [00:40:49]: I think we've actually done a lot of work in the past and people can see kind of our blog posts about sort of self-supervised learning and some other kind of vision-related stuff in the past as well. So we're very familiar with, with that stuff. But I think our main focus right now is on kind of, as we say, coding and reasoning. And there, there's certainly a visual component to some problems. But, you know, it's not necessarily required for all problems. And actually we found that for most of the kind of like code writing and, and reasoning problems that we care about, the visual part isn't really a huge important part of it. Sometimes if you really need to, you can maybe describeSWYX [00:41:24]: the thing.JOSH [00:41:24]: There are other like, you know, multimodal models that you can use off the shelf to sort of plug in for those particular piecesSWYX [00:41:30]: that you need, right?JOSH [00:41:30]: Like if something is driving a browser or whatever, like you can sometimes get away with not having to have that baked into the original model. So our folk were, you know, in a sense, we kind of do a lot across the stack. We're working on our own infrastructure and pre-training and RL and fine tuning and products and everything. But in another sense, we're very narrowly focused on the application side. So all of the stuff across the stack is kind of going toward a very particular purpose. And so that particular purpose right now doesn't really need vision. So we think that people are going to make all sorts of really cool image modelsSWYX [00:42:00]: like Jonathan, right?JOSH [00:42:00]: And all sorts of interesting multimodal models into the future. We'll let them go do that. That's great. We'll take advantage of that, partner with those people in the future. And right now we're really focused on kind of the core reasoning and coding capabilities and aspects of the model.SWYX [00:42:14]: I wanted to go into carbs since that's kind of the next layer of the stack. We talked about carbs in the first episode with Kanjin because you've actually had a blog post about it like a couple of years ago. Maybe let's introduce it.JONATHAN [00:42:26]: Has that been a couple of years now?JOSH [00:42:28]: No, it must have been at least one year. Hopefully it's not multiple years.SWYX [00:42:32]: Sorry, I'm counting AI time. Yeah, yeah. Yeah, I was going to sayJONATHAN [00:42:35]: you're making me feel really old right now.SWYX [00:42:39]: I count everything before the generally intelligent rename as like, you know, prehistory. Yeah. And now sort of modernity, right? So I actually thought carbs was more about hyperparameter optimization in a sense of like sort of parameters, hyperparameter search. Whereas, you know, when you introduced it, especially in this blog post, it's more about scaling laws and predictability of like, are we sort of in the right ballpark before we scale things up? Maybe sort of recount the history of carbs.JOSH [00:43:10]: Yeah, so it really is a little bit of both. So carbs is, it's maybe a backronym, but it's for cost aware Pareto region Bayesian search. So this is about technically how it works, but carbs is like, you know, we like pastries and stuff.SWYX [00:43:26]: So great, why not? But the point is thatJOSH [00:43:29]: it's a cost aware hyperparameter tuner. So most hyperparameter tuners, you kind of say, OK, here's this objective function. I want you to make this number as big as possible or as small as possible, whichever direction you want to go. So yeah, just go make this number, you know, as small as possible. OK, so it'll try a bunch of differentSWYX [00:43:46]: hyperparameters,JOSH [00:43:46]: a bunch of different configurationsSWYX [00:43:48]: to figure out, like,JOSH [00:43:48]: how do I tweak your network and architecture, et cetera, to get the kind of best performance I possibly can. That's usually saying, like, you know, almost all of these hyperparameter configurations are, let's say they're all going to use the same number of GPUs or the same number of nodes.SWYX [00:44:01]: So it's going to runJOSH [00:44:01]: for the same amount of time.SWYX [00:44:03]: So you can do that.JOSH [00:44:03]: You can get a number out and that's great. But what carbs does is it says,SWYX [00:44:07]: OK, actually,JOSH [00:44:07]: what if we relax that constraint? What if we say each of these different points, we're going to model how expensive it will be to sample this configuration. So if what if we train with just one one hundredth of the data? Like, how well can we do?SWYX [00:44:19]: What if we trainJOSH [00:44:19]: with one tenth of the data? What if we train with all the data? That way you can understand, like, as we get more and more data, as we spend more and more compute,SWYX [00:44:26]: as we make a biggerJOSH [00:44:26]: and bigger network, how does performance change with these things that change? Like how expensive it is to even explore this data point. So by doing that, we can see the scaling laws for not just, you know,SWYX [00:44:36]: the scaling lawsJOSH [00:44:36]: from like the, you know, Chantilla paper, the scaling laws for all parameters. We can see how does how does the number of layers change with this? How does the, you know, the learning rate change? How do the like, you know, various types of regularization change? So you can see these nice scaling laws. And as you're going across costs, like how should this be changing as you're scaling up your model? So that, coupled with the kind of metric that we chose, which is a very precise way of measuring performance, allowed us to really like hone in on parameters that worked really wellSWYX [00:45:05]: and understand, like,JOSH [00:45:05]: how do we want to scale those up, especially as we're changingSWYX [00:45:08]: things about the network?JOSH [00:45:08]: Like one of the things that we did is we used a custom tokenizer. As we change this tokenizer, changes a bunch of other things about the model. So how should we scale up this entirely new tokenizer? Like no one has ever made a model this large with this tokenizer before. And so how do we want toSWYX [00:45:22]: change all these things?JOSH [00:45:22]: Harps kind of shows you, like, look, as you change these parameters, like these other ones are kind of dependent on this.SWYX [00:45:28]: Like this is the, these areJOSH [00:45:28]: the relationships between them. So you can better understand, like, OK, if I'm going to scale this up 10x or 100x, like, where do I want to be? I can only go so far. And so, you know, we did run, like, I think maybe it was like a 14b one or somethingSWYX [00:45:40]: like that to check.JOSH [00:45:41]: But and so we had a bunch of like 1b or 14b and then at 70b. I don't think we had a, I think we just did like one at 14b. So you can, we get to check that like, oh, is this on the curve? Like, is this where we expect? It was like right there. So then great, go on to the next one. Yeah, I mean, that makes a lot of sense.SWYX [00:45:56]: I wonder if, so one of the key questions, and correct me if I'm wrong, but like usually people do search or do their evals just based on loss. But you actually evaluate based on, you know, the sort of end state evals that people might expect, like HellaSwag and Lombata, whatever. What is the norm here? Is there a norm?JOSH [00:46:20]: Yeah, I don't know if there's a hundred percent.SWYX [00:46:21]: I don't know. I only see loss on most people's reports.JOSH [00:46:25]: I think it's easy to, like, loss is very nice because it's very precise. It will tell you, like, very fine grained differences between like really small changes in your hyperparameters or network architecture. Whereas, especially at the smaller scales, if you're looking at like accuracy, it's very noisy. Like it might be zero or a hundred or like, you know, fluctuating by like 10 or 20 percentage points, which makes it really hard to tell, like, did that change actually mean anything? So our loss is sort of a combination of these two. Instead of saying, like, let's just look at perplexity, we say, let's look at perplexity on the tasks that we care about for multiple choice questions effectively.SWYX [00:47:00]: So we're saying like, yes,JOSH [00:47:00]: this is formulated as a multiple choice question, and we're going to look at the, like, you know, the loss of perplexity for this particular answer token. And that ends up being something that's like both targeted to what you actually care about and also very precise. The nice thing about this though is that it's independent of the data that you train on. One thing that's annoying about perplexity or about loss is that as you change your data set, this is really obnoxious because now it fundamentally changes your loss, right? And so you can't tell, like, how do I tweak my data set? But because we have this held out evaluation dat
FREE PATREON TEASER: On episode 187, Eric and Josh each pick a best(?) and worst way to die. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
On Episode 186, Eric tells Josh all about Whale Falls, how they create ecosystems on the ocean floor, and the adaptive radiation that blossoms from these environments. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
FREE PATREON TEASER: On episode 185, Eric and Josh are swapping articles as they Super Heavy Elements and What's Going On With Politics in Germany? Plus, kid cuisine and E.T. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
On Episode 184, Eric and Josh discuss what happens to the human body when it experiences the forces involved during a plane crash. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
FREE PATREON TEASER: The Finale is here. On Episode 183, Eric and Josh breakdown Season 1, Episode 8 of Netflix's "3 Body Problem" created by David Benioff, D. B. Weiss and Alexander Woo, based on the Hugo Award winning Chinese novel The Three-Body Problem by Liu Cixin. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
Der Jupitermond Io erzeugt einen Ring aus Plasma entlang seiner Umlaufbahn. Warum er das tut und was das mit der Kernfusion zu tun hat, erfahrt ihr in der neuen Folge der Sternengeschichten: https://astrodicticum-simplex.at/?p=36914 Wer den Podcast finanziell unterstützen möchte, kann das hier tun: Mit PayPal (https://www.paypal.me/florianfreistetter), Patreon (https://www.patreon.com/sternengeschichten) oder Steady (https://steadyhq.com/sternengeschichten)
On Episode 182, Eric and Josh discuss the cultural phenomenon and scientific research regarding Repressed Memories. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
FREE PATREON TEASER: On Episode 181, Eric and Josh breakdown Season 1, Episodes 6-7 of Netflix's "3 Body Problem" created by David Benioff, D. B. Weiss and Alexander Woo, based on the Hugo Award winning Chinese novel The Three-Body Problem by Liu Cixin. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
On Episode 180, Eric and Josh discuss the history and present day applications of Nano Fiber Technology. Plus, could Nano Fibers do what they did to Judgement Day in "3 Body Problem"? WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
FREE EXTENDED PATREON TEASER: On Episode 179, Eric and Josh are joined by special guest Justin Bell to breakdown Season 1, Episodes 4-5 of Netflix's "3 Body Problem" created by David Benioff, D. B. Weiss and Alexander Woo, based on the Hugo Award winning Chinese novel The Three-Body Problem by Liu Cixin. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus to hear the second half of this episode. You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
On Episode 178, Josh took an Easter trip to Los Angeles and got the grand tour from Eric. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
PATREON EXCLUSIVE: On Episode 177, Eric and Josh breakdown Season 1, Episodes 2-3 of Netflix's "3 Body Problem" created by David Benioff, D. B. Weiss and Alexander Woo, based on the Hugo Award winning Chinese novel The Three-Body Problem by Liu Cixin. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
PATREON EXCLUSIVE: On Episode 176, Eric and Josh breakdown Season 1, Episode 1 of Netflix's "3 Body Problem" created by David Benioff, D. B. Weiss and Alexander Woo, based on the Hugo Award winning Chinese novel The Three-Body Problem by Liu Cixin. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
On Episode 175, Eric and Josh breakdown the tangled world of Knot Theory. WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
UNLOCKED PATREON: The Counting puppet: Blah ah ah! Count the bodies. One, Two, Three! Ah Ah Ah! [Suddenly Big Blird breaks through the door with a full SWAT team following] Big Blird: DROP THE BODIES, COUNT! Hey-llo all, Josh and eric are doing things a little differently this week. They're giving a little taste of this prem content to the regular listeners on the wide feed. Forgive us, all mighty patrons! With the release of Neckflix's new Three Body Problem on 3/21, the fellas give a preview of their hopes and fears in turning this pretty damn good Scifi trilogy into a mass marketed show. Will it be good? Will it be bad? It's made by the Game of Thrones couple, so it's sure to be something! WE HAVE A PATREON! Subscribe at https://www.patreon.com/RidingTheTorus You can find Eric's research notes for every episode here: https://drive.google.com/folderview?id=1syBwRsJ3b3YnOlUCXXFEEUpgF0NODLL2 Also! If you enjoy the Riding The Torus theme song, you can now download it for FREE from the Bueno Tornado bandcamp page. Here is the link: http://buenotornado.bandcamp.com/track/riding-the-torus-theme Hosts: eric beal - instagram.com/ericbealart/ Josh Campbell - twitter.com/josh_campbell
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