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tensorflow
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3 | 223 | |
7 | 182,857 | |
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0.0 | 10.0 | |
over 2 years ago | 4 days ago | |
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- | Apache License 2.0 |
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website
- How do I get started with Jax on TPU VMs
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GPT-J “the open source cousin of GPT-3 everyone can use”
Your view here is entirely reasonable. It was my view before I ever heard about TFRC. I was every bit as skeptical.
That view is wrong. From https://github.com/shawwn/website/blob/master/jaxtpu.md :
> So we're talking about a group of people who are the polar opposite of any Google support experience you may have had.
> Ever struggle with GCP support? They took two weeks to resolve my problem. During the whole process, I vividly remember feeling like, "They don't quite seem to understand what I'm saying... I'm not sure whether to be worried."
> Ever experience TFRC support? I've been a member for almost two years. I just counted how many times they failed to come through for me: zero times. And as far as I can remember, it took less than 48 hours to resolve whatever issue I was facing.
> For a Google project, this was somewhere between "space aliens" and "narnia" on the Scale of Surprising Things.
[...]
> My goal here is to finally put to rest this feeling that everyone has. There's some kind of reluctance to apply to TFRC. People always end up asking stuff like this:
> "I'm just a university student, not an established researcher. Should I apply?"
> Yes!
> "I'm just here to play around a bit with TPUs. I don't have any idea what I'm doing, but I'll poke around a bit and see what's up. Should I apply?"
> Heck yeah!
> "I have a Serious Research Project in mind. I'd like to evaluate whether the Cloud TPU VM platform is sufficient for our team's research goals. Should I apply?"
> Absolutely. But whoever you are, you've probably applied by now. Because everyone is realizing that TFRC is how you accomplish your research goals.
I expect that if you apply, you'll get your activation email within a few hours. Of course, you better get in quick. My goal here was to cause a stampede. Right now, in my experience, you'll be up and running by tomorrow. But if ten thousand people show up from HN, I don't know if that will remain true. :)
I feel a bit bad to be talking at length at TFRC. But then I remembered that none of this is off-topic in the slightest. GPT-J was proof of everything above. No TFRC, no GPT-J. The whole reason that the world can enjoy GPT-J now is because anyone can show up and start doing as many effective things as you can possibly learn.
It was all thanks to TFRC, the Cloud TPU team, the JAX team, the XLA compiler team -- hundreds of people, who have all managed to gift us this amazing opportunity. Yes, they want to win the ML mindshare war. But they know the way to win it is to care deeply about helping you achieve every one of your research goals.
Think of it like a side hobby. Best part is, it's free. (Just watch out for the egress bandwidth, ha. Otherwise you'll be talking with GCP support for your $500 refund -- and yes, that's an unpleasant experience.)
tensorflow
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Side Quest Devblog #1: These Fakes are getting Deep
# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")
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Google lays off its Python team
[3]: https://github.com/tensorflow/tensorflow/graphs/contributors
- TensorFlow-metal on Apple Mac is junk for training
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐
- One .gitignore to rule them all
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10 Github repositories to achieve Python mastery
Explore here.
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GitHub and Developer Ecosystem Control
Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.
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Non-determinism in GPT-4 is caused by Sparse MoE
Right but that's not an inherent GPU determinism issue. It's a software issue.
https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.
Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.
Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.
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Can someone explain how keras code gets into the Tensorflow package?
and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:
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Is it even possible to design a ML model without using Python or MATLAB? Like using C++, C or Java?
Exactly what language do you think TensorFlow is written in? :)
What are some alternatives?
mesh-transformer-jax - Model parallel transformers in JAX and Haiku
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
helpmecode - Augmented Intelligence Programming
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
swarm-jax - Swarm training framework using Haiku + JAX + Ray for layer parallel transformer language models on unreliable, heterogeneous nodes
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
scikit-learn - scikit-learn: machine learning in Python
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.