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2023年10月9日 · 本文将从框架设计API的角度理解JAX的API，并且介绍它与PyTorch相关API的转换。 有了这些概念之后，我们就能比较轻松地看懂JAX的代码，能够在PyTorch与JAX之间自由切换了。 PyTorch的 nn.Module 相关API广受欢迎，就是因为它捕捉了深度学习模型训练过程中的核心步骤。 概括来说，PyTorch里面有六个核心API： model = Model(arg_model) # 1. 模型初始化. opt = Optimizer(arg_opt, model.parameters()) # 2. 优化器初始化. y = model(x) # 3. 模型计算. …

Grain is a library for reading data for training and evaluating JAX models. It’s open source, fast and deterministic. Users can bring arbitrary Python transformations. Grain is designed to be modular. Users can readily override Grain components if need be with their own implementation.

2024年6月28日 · I recommend precompiling a program using JAX/Python, saving the compiled executable, then using the PJRT C API to deserialize, load, and execute that program. Here are some pointers (sorry don't have time to write this all up atm):

有两种方法可以安装支持 NVIDIA GPU 的 JAX. JAX 团队强烈建议使用 pip wheel 安装 CUDA 和 cuDNN，因为它更容易！ NVIDIA 仅为 x86_64 和 aarch64 发布了 CUDA pip 软件包；在其他平台上，您必须使用 CUDA 的本地安装。 # NVIDIA CUDA 12 installation # Note: wheels only available on linux. 如果 JAX 检测到错误版本的 NVIDIA CUDA 库，则需要检查以下几项. 确保未设置 LD_LIBRARY_PATH，因为 LD_LIBRARY_PATH 会覆盖 NVIDIA CUDA 库。 确保安装的 …

Secondly, the iteratively applied Layer Normalization across layers can lead to very high gradients during the first iterations, which can be solved by using Pre-Layer Normalization (similar to Pre-Activation ResNet), or replacing Layer Normalization by other techniques (Adaptive Normalization, Power Normalization).

2024年9月28日 · You can download a AOSP gsi or import the GSI from your device. It then creates a new dynamic partition, loads the GSI onto the new partition, and then your phone boots into your desired GSI. It works for Android 10 and newer.

GPJax is a didactic Gaussian process (GP) library in JAX, supporting GPU acceleration and just-in-time compilation. We seek to provide a flexible API to enable researchers to rapidly prototype and develop new ideas.

This tutorial demonstrates how to use JAX/Flax for LLM pretraining via data and tensor parallelism. It is originally inspired by this Keras miniGPT tutorial. We will use Google TPUs and SPMD to train a language model miniGPT. Instead of using a GPU, you should use the free TPU on Colab or Kaggle for this tutorial. Setup# Install JAX and Flax first.

This notebook explored efficient methods for loading data on a GPU with JAX, using libraries such as PyTorch DataLoader, TensorFlow Datasets, Grain, and Hugging Face Datasets. You also learned GPU-specific optimizations, including using device_put for data transfer and managing GPU memory, to enhance training efficiency.