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Install PyTorch lightning

Step 0: Install. Simple installation from PyPI. pip install pytorch-lightning Step 1: Add these imports import os import torch from torch import nn import torch.nn.functional as F from torchvision.datasets import MNIST from torch.utils.data import DataLoader, random_split from torchvision import transforms import pytorch_lightning as p PyTorch Lightning was used to train a voice swap application in NVIDIA NeMo- an ASR model for speech recognition, that then adds punctuation and capitalization, generates a spectrogram and regenerates the input audio in a different voice conda install noarch v1.3.5; To install this package with conda run: conda install -c conda-forge pytorch-lightning Step 0: Install PyTorch Lightning; Step 1: Define LightningModule; Step 2: Fit with Lightning Trainer; Basic features. Manual vs automatic optimization. Automatic optimization; Manual optimization; Predict or Deploy. Option 1: Sub-models; Option 2: Forward; Option 3: Production; Using CPUs/GPUs/TPUs; Checkpoints; Data flow; Logging; Optional extensions. Callback

Simple installation from PyPI. pip install lightning-bolts. Install bleeding-edge (no guarantees) pip install git+https://github.com/PytorchLightning/lightning-bolts.git@master --upgrade. In case you want to have full experience you can install all optional packages at once. pip install lightning-bolts [ extra In its true sense, Lightning is a structuring tool for your PyTorch code. You just have to provide the bare minimum details (Eg. number of epoch, optimizer, etc). The rest will be automated by Lightning. Lightning reduces the amount of work needed to be done (By @neelabh If you are using an earlier version of PyTorch Lightning uses Apex to support 16-bit. Follow these instructions to install Apex. To use 16-bit precision, do two things: Install Apex. Set the precision trainer flag

Install PyTorch. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.9 builds that are generated nightly pip install ray[tune] To use Ray Tune with PyTorch Lightning, we only need to add a few lines of code!! Getting started with Ray Tune + PTL! To run the code in this blog post, be sure to first run: pip install ray[tune] pip install pytorch-lightning>=1.0 pip install pytorch-lightning-bolts>=0.2. *Codecov is > 90%+ but build delays may show less PyTorch Lightning is just organized PyTorch Lightning Design Philosophy Continuous Integration How To Use Step 0: Install Install with optional dependencies Conda Install stable 1.3.x Install bleeding-edge - future 1.4 Step 1: Add these imports Step 2: Define a LightningModule (nn.Module subclass) Step 3: Train

For Colab users, then you can solve this by reinstalling (or upgrading) pytorch_lightning version 1.3.0dev without any dependencies except fsspec. !pip install git+https://github.com/PyTorchLightning/pytorch-lightning fsspec --no-deps --target=$nb_path - Kyle_397 Mar 9 at 2:3 First, install Bolts: pip install pytorch-lightning-bolts 2. Import the model and instantiate it: We specify the number of input features, the number of classes and whether to include a bias term (by default this is set to true). For the Iris dataset, we would specify in_features=4 and num_classes=3. You can also specify a learning rate, L1 and/or L2 regularization. 3. Load the data, which can.

pip install git+https://github.com/PytorchLightning/lightning-bolts.git@master --upgrade In case you want to have full experience you can install all optional packages at once pip install lightning-bolts[ extra 1. I installed pytorch-lightning using pip, and I'm running on Mac. I tried: ! pip install pytorch-lightning --upgrade ! pip install pytorch-lightning-bolts. (finished successfully) and then: import pytorch_lightning as pl. and what I get is: -- Installing PyTorch Lightning: Installing Lightning is same as that of any other library in python. pip install pytorch-lightning or if you want to install it in a conda environment you can use the following command:-conda install -c conda-forge pytorch-lightning PyTorch Lightning Model Format: If you have ever used PyTorch you must know that defining PyTorch model follows the following format. To install a previous version of PyTorch via Anaconda or Miniconda, replace 0.4.1 in the following commands with the desired version (i.e., 0.2.0). Installing with CUDA 9. conda install pytorch=0.4.1 cuda90 -c pytorch. or. conda install pytorch=0.4.1 cuda92 -c pytorch. Installing with CUDA 8 . conda install pytorch=0.4.1 cuda80 -c pytorch. Installing with CUDA 7.5. conda install. We will need both pip and pip3 to install pytorch lightning $ sudo apt-get update $ sudo apt-get install libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev liblapack-dev libblas-dev gfortran $ sudo apt-get install python-pip $ sudo pip3 install -U pip testresources setuptools==49.6.0 # This step wasn't necessary but was done during the installation process for python 2 pi

Introducing Lightning Transformers, a new library that seamlessly integrates PyTorch Lightning, HuggingFace Transformers and Hydra, to scale up deep learning research across multiple modalities. PyTorch Lightning team. Apr 21 · 6 min read. Transformers are increasingly popular for SOTA deep learning, gaining traction in NLP with BeRT based. conda install. conda install pytorch-lightning -c conda-forge. For me, I prefer to use anaconda for my python interpreter, its more complete for deep learning and data science people. It's ready. The package is built on PyTorch Lightning to allow training on CPUs, single and multiple GPUs out-of-the-box. If you do not have pytorch already installed, follow the detailed installation instructions. Otherwise, proceed to install the package by executing. pip install pytorch-forecasting. or to install via conda . conda install pytorch-forecasting pytorch >= 1.7-c pytorch-c conda-forge. Vist. >python -m pip install pytorch-lightning Successfully installed fsspec-0.8.5 pytorch-lightning-1.1.2 tensorboard-2.4.0 tensorboard-plugin-wit-1.7. tqdm-4.55.0 >python -m pip install tensorboard Requirement already satisfied:... >>> import tensorboard >>> tensorboard.__version__ '2.4.0' >python Python 3.7.5 (default, Oct 31 2019, 15:18:51) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32 Type help, copyright, credits or license for more information. >>> import.

pytorch-lightning · PyP

PyTorch Lightnin

  1. Lightning Flash is a library from the creators of PyTorch Lightning to enable quick baselining and experimentation with state-of-the-art models for popular Deep Learning tasks. We are excited to announce the release of Flash v0.3 which has been primarily focused on the design of a modular API to make it easier for developers to contribute and.
  2. TorchMetrics in PyTorch Lightning¶. TorchMetrics was originaly created as part of PyTorch Lightning, a powerful deep learning research framework designed for scaling models without boilerplate.. While TorchMetrics was built to be used with native PyTorch, using TorchMetrics with Lightning offers additional benefits
  3. First you need to install the library. It has been tested with python 3.6+, with the latest versions of pytorch-lightning. If you want to create a new conda environment, run: conda env create-n adaenv python = 3.7 conda activate adaenv. Install the library (with developer mode if you want to develop your own models later on, otherwise you can skip the -e): pip install-e adalib. Note: on.
  4. PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research. With Neptune integration you can: monitor model training live, log training, validation, and testing metrics, and visualize them in the Neptune UI, log hyperparameters, monitor hardware usage, log any additional metrics, log performance charts and images, save model checkpoints. Installation. To install.
  5. Hence, we do it here if necessary! pip install pytorch-lightning == 1.3.4 import pytorch_lightning as pl from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint # Path to the folder where the datasets are/should be downloaded (e.g. CIFAR10) DATASET_PATH =./data # Path to the folder where the pretrained models are saved CHECKPOINT_PATH =./saved_models/tutorial7.

Pytorch Lightning :: Anaconda

PyTorch Lightning. Another approach for creating your PyTorch based MLP is using PyTorch Lightning. It is a library that is available on top of classic PyTorch (and in fact, uses classic PyTorch) that makes creating PyTorch models easier. The reason is simple: writing even a simple PyTorch model means writing a lot of code. And in fact, writing. Installation ¶ If you are working windows, you need to first install PyTorch with Create a pytorch_lightning.Trainer() object. Find the optimal learning rate with its .tuner.lr_find() method. Train the model with early stopping on the training dataset and use the tensorboard logs to understand if it has converged with acceptable accuracy. Tune the hyperparameters of the model with your.

PyTorch Lightning 1.0: PyTorch, nur schneller und flexibler Mit einer stabilen API tritt das auf PyTorch basierende Framework an, auch komplexe Deep-Learning-Modelltrainings einfach und skalierbar. This documentation applies to the legacy Trains versions. For the latest documentation, see ClearML. Integrate Trains into the PyTorch code you organize with pytorch-lightning. Use the PyTorch Lightning TrainsLogger module. Also, see the PyTorch Lightning Trains Module documentation. To install Trains: pip install trains. By default, Trains. How do I use DeepSpeed with PyTorch Lightning? We first need to install DeepSpeed. pip install deepspeed. After installing this dependency, PyTorch Lighting provides quick access to DeepSpeed through the Lightning Trainer. Below are a couple of code examples demonstrating how to take advantage of DeepSpeed in your Lightning applications without the boilerplate. DeepSpeed ZeRO Stage 2 (Default. Fortunately, PyTorch lightning gives you an option to easily connect loggers to the pl.Trainer and one of the supported loggers that can track all of the things mentioned before (and many others) is the NeptuneLogger which saves your experiments in you guessed it Neptune

Lightning in 2 steps — PyTorch Lightning 1

pytorch-lightning. PyTorch Lightning helps organize PyTorch code and decouple the science code from the engineering code. It's more of a style-guide than a framework. By organizing PyTorch code under a LightningModule, Lightning makes things like TPU, multi-GPU and 16-bit precision training (40+ other features) trivial.. For more information, please see Simple installation from PyPIbashpip install pytorch-lightning. Docs. master; 0.7.5; 0.7.3; 0.7.1; 0.6.0; 0.5.3.2; Demo. MNIST, GAN, BERT, DQN on COLAB! MNIST on TPUs. What is it? READ THIS QUICK START PAGE. Lightning is a way to organize your PyTorch code to decouple the science code from the engineering.It's more of a PyTorch style-guide than a framework. In Lightning, you organize your code. Evaluating your PyTorch Lightning model. Today, many engineers who are used to PyTorch are using PyTorch Lightning, a library that runs on top of classic PyTorch and which helps you organize your code. Below, we'll also show you how to evaluate your model when created with PyTorch Lightning PyTorch Lightning aims for users to focus more on science and research instead of worrying about how they will deploy the complex models they are building. Sometimes some simplifications are made to models so that the model can run on the computers available in the company. However, by using cloud technologies, PyTorch Lightning allows users to debug their model which normally requires 512. TL;DR This post outlines how to distribute PyTorch Lightning training on Distributed Clusters with Azure ML In my last few posts on the subject, I outlined the benefits of both PyTorch Lightning.

pytorch-lightning-bolts · PyP

  1. Language Modeling Example with Pytorch Lightning and Huggingface Transformers. Language modeling fine-tuning adapts a pre-trained language model to a new domain and benefits downstream tasks such as classification. The script here applies to fine-tuning masked language modeling (MLM) models include ALBERT, BERT, DistilBERT and RoBERTa, on a text dataset. Details about the models can be.
  2. g. With PyTorch now adding support for mixed precision and with PL, this is really easy to implement
  3. Using PyTorch Lightning with Tune To avoid that each training instance downloads their own MNIST dataset, we download it once and share the data_dir between runs. config = {layer_1_size: tune. choice ([32, 64, 128]), We also delete this data after training to avoid filling up our disk or memory space. Configuring the search space¶ Now we configure the parameter search space. We would.
  4. Installing Ray. Tune is part of Ray, an advanced framework for distributed computing. It is available as a PyPI package and can be installed like this: 1 pip install ray[tune] pytorch-lightning. Setting up the LightningModule. To use Ray Tune with PyTorch Lightning, we only need to add a few lines of code. Best of all, we usually do not need to change anything in the LightningModule! Instead.
  5. mnist_pytorch_lightning. # flake8: noqa # yapf: disable # __import_lightning_begin__ import math import torch import pytorch_lightning as pl from filelock import FileLock from torch.utils.data import DataLoader, random_split from torch.nn import functional as F from torchvision.datasets import MNIST from torchvision import transforms import os.
How To Develop With PyTorch At Lightning Speed - Global

Installing. Pytorch-lightning and W&B are easily installable via pip. pip install pytorch-lightning wandb. We just need to import a few Pytorch-Lightning modules as well as the WandbLogger and we are ready to define our model. ️ Defining our model with LightningModule. Research often involves editing the boiler plate code with new experimental variations. Most of the errors get. Credit to original author William Falcon, and also to Alfredo Canziani for posting the video presentation: Supervised and self-supervised transfer learning (with PyTorch Lightning) In the video presentation, they compare transfer learning from pretrained conda install pytorch-lightning -c conda-forge The research The Model. Lightning由以下核心部分组成: The model; The optimizers; The train/val/test steps; 我们通过Model引入这一部分,下面我们将会设计一个三层的神经网络模型. import torch from torch.nn import functional as F from torch import nn from pytorch_lightning.core.lightning import LightningModule. PyTorch Lightning¶ Horovod is supported as a distributed backend in PyTorch Lightning from v0.7.4 and above. With PyTorch Lightning, distributed training using Horovod requires only a single line code change to your existing training script

Getting Started with PyTorch Lightning Learn OpenC

  1. pip install pytorch-lightning-bolts. We couldn't find any similar packages Browse all packages. Package Health Score. 84 / 100. Popularity. Recognized. Maintenance. Healthy. Security . No known security issues. Community. Active. Make sure the packages you're using are safe to use. Secure my Project . Popularity. Recognized. Popularity by version Total Weekly Downloads (4,489) Dependents 0.
  2. PyTorch Lightning is a Python package that provides interfaces to PyTorch to make many common, but otherwise code-heavy tasks, more straightforward. This includes training on multiple GPUs. The following is the same tutorial from the section above, but using PyTorch Lightning instead of explicitly leveraging the DistributedDataParallel class: File : pytorch-ddp-test-pl.sh #!/bin/bash #SBATCH.
  3. BERT masked LM training. Aug 15, 2020. Initial Setup. I will use BERT model from huggingface and a lighweight wrapper over pytorch called Pytorch Lightning to avoid writing boilerplate.! pip install transformers ! pip install pytorch-lightning
  4. Simple installation from PyPIbashpip install pytorch-lightning. Other installation options #### Install with optional dependencies. bash pip install pytorch-lightning['extra'] #### Conda. bash conda install pytorch-lightning -c conda-forge #### Install stable - future 1.1.x. the actual status of 1.1 [stable] is following

Pytorch Lightning comes with a lot of features that can provide value for both professionals, as well as newcomers in the field of research. All you need to do is pip install the nep t une-client library, and then you simply call the NeptuneLogger from ignite.contrib.handlers.neptune_logger. from ignite.contrib.handlers.neptune_logger import * npt_logger = NeptuneLogger(api_token. Focus on. Machine Learning, Not Infrastructure. The value you can provide with AI is limited by how fast you iterate through ideas. Things like clunky infrastructure and slow data loading only distract from your job. We created Grid to fix that. Whether you are building a production-grade AI pipeline, doing drug discovery, or pushing the SOTA. Pytorch-lightning + LSTM. ¶. Following is an example how one can rewrite pytorch LSTM model in a Lightning form. The goal is to create 'trainer' instance from Trainer class of pytorch lightning. A Trainer consists of System part and DataModule part. System part consists of Model part (including forward method) and training part (including loss.

Image Self-Supervised Training With PyTorch Lightning. May 25, 2020 · 13 minute read. (You can also view this post in Google Colab) Self-Supervision is the current hotness of deep learning. Yes, deep networks and transfer learning are now old hat — you need to include self-supervised somewhere if you want to get those big VC dollars PyTorch Lightning is a lightweight wrapper for organizing your PyTorch code and easily adding advanced features such as distributed training and 16-bit precision. It retains all the flexibility of PyTorch, in case you need it, but adds some useful abstractions and builds in some best practices from torchtext.data import BucketIterator. from pytorch_lightning.core.lightning import LightningModule. from pytorch_lightning import Trainer. from pytorch_lightning.loggers import TensorBoardLogger. import pandas as pd. import numpy as np. [ ] text_field = Field (sequential=True, include_lengths=True, fix_length=200) label_field = Field.

16-bit training — PyTorch Lightning 1

  1. PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. It is a lightweight and high-performance framework that organizes PyTorch code to decouple the research from the engineering, making deep learning experiments easier to read and reproduce
  2. conda activate my_env pip install pytorch-lightning. Licence. Please observe the Apache 2.0 license that is listed in this repository. In addition the Lightning framework is Patent Pending. BibTeX. If you want to cite the framework feel free to use this (but only if you loved it ?): @article{falcon2019pytorch, title={PyTorch Lightning}, author={Falcon, WA}, journal={GitHub. Note: https.
  3. Install IceVision. This will include the IceData package that provides easy access to several sample datasets, as well as the engines and libraries that IceVision works with. 2. Download and prepare a dataset to work with. 3. Select an object detection library, model, and backbone. 4. Instantiate the model, and then train it with both the fastai and pytorch lightning engines. 5. And finally.
  4. Installation. DeepForest has Windows, Linux and OSX prebuilt wheels on pypi. We strongly recommend using a conda or virtualenv to create a clean installation container. DeepForest is also available on conda-forge to help users compile code and manage dependencies. Conda builds are currently available for windows, osx and linux, python 3.6 or 3.7
  5. conda install pytorch==1.5.1 torchvision==0.6.1 cpuonly -c pytorch [For conda on macOS] Run conda install and specify PyTorch version 1.5.1. There is only one command to install PyTorch 1.5.1 on macOS: conda install pytorch==1.5.1 torchvision==0.6.1 -c pytorch [For pip] Run pip3 install by specifying version with -
  6. pip install gpytorch. For more instructions, see the Github README. Examples. Browse Examples. Documentation. Browse Docs. To learn about GPyTorch's inference engine, please refer to our NeurIPS 2018 paper: GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. ArXiV BibTeX. The Team. Geoff Pleiss . Jacob R. Gardner. Kilian Q. Weinberger. Andrew Gordon Wilson. Max.
  7. Read our launch blogpost Pip / conda pip install lightning-flash -U Pip from source pip install github.com. Consistent with PyTorch Lightning's goal of getting rid of the boilerplate, Flash aims to make it easy to train, inference, and fine-tune deep learning models. Flash is built on top of PyTorch Lightning to abstract away the unnecessary boilerplate for common Deep Learning Tasks.

PyTorc

py37-pytorch-lightning. The lightweight PyTorch wrapper for high-performance AI research. View on GitHub. Port Health: 11_x86_64 11_arm64 10.15 10.14 10.13 10.12 10.11 10.10 10.9 10.8 10.7 10.6_i386 10.6 10.5_ppc. Summary; Build Information; Installation Stats; Trac Tickets ; Quick Links: About; Statistics FAQ; Git Repository; Documentation; API; Feature Requests; Issues; MacPorts. MacPorts. Losing the Boiler Plate PyTorch Lightning Design Philosophy Explained 1. Self Contained Models and Data. One of the traditional bottlenecks to reproducibility in deep learning is that models are often thought of as just a graph of computations and weights. Example PyTorch Computation Graph from the PyTorch AutoGrad Docs. In reality, reproducing Deep Learning requires mechanisms to keep track. Check the download stats of pytorch-lightning-bolts library. It has a total of 139701 downloads Download Effective Feature Management. Tracking PyTorch Lightning Experiments Using NeptuneAI by@neptuneAI_jakub. Tracking PyTorch Lightning Experiments Using NeptuneAI . August 1st 2020 @neptuneAI_jakubneptune.ai Jakub Czakon. Senior data scientist building experiment tracking tools for ML projects at https://neptune.ai. Working with PyTorch Lightning and wondering which logger should you. Finally, install PyTorch and PyTorch Lightning. The instructions below can vary depending on whether you have a CUDA-enabled machine, Linux, etc. In general, follow the instructions from the website. conda install-y pytorch torchvision torchaudio cudatoolkit = 10.2 -c pytorch -c conda-forge conda install-y pytorch-lightning -c conda-forg

Variational Autoencoder Demystified With PyTorch

Download ZIP. Minimal Example for bug report for Pytorch-Lightning Raw gistfile1.txt import argparse: from pathlib import Path: from typing import Tuple : import torch: import torch.nn.functional as F: from torch import Tensor: from torch.utils.data import Dataset, DataLoader: import pytorch_lightning as pl: from pytorch_lightning import Trainer, seed_everything: from pytorch_lightning. We download the coco dataset which contains 5 captions per image and has roughly 82k images. We take 20% of it to be our validation set. Considering that the image backbone is trained using imagenet, we normalise it using the imagenet stats as shown in the transforms normalize step. We also resize the image to 128x128 to make sure it trains in reasonable time. Warning: Downloading the files. PyTorch Lightning is a Python package that provides interfaces to PyTorch to make many common, but otherwise code-heavy tasks, more straightforward. This includes training on multiple GPUs. The following is the same tutorial from the section above, but using PyTorch Lightning instead of explicitly leveraging the DistributedDataParallel class: File : pytorch-ddp-test-pl.sh #!/bin/bash #SBATCH. Download AUR Home; Packages According to requirements.txt of pytorch_lightning, it should be added into depends of PKGBUILD. If it is missing, the code will raise ModuleNotFoundError: No module named 'torchmetrics' when you used metrics from pytorch_lightning. 7Z0nE commented on 2021-03-10 09:40. @hottea tensorboard should not be an optdepend, as it is imported by many pl modules even if. We'll fine-tune BERT using PyTorch Lightning and evaluate the model. Multi-label text classification (or tagging text) is one of the most common tasks you'll encounter when doing NLP. Modern Transformer-based models (like BERT) make use of pre-training on vast amounts of text data that makes fine-tuning faster, use fewer resources and more accurate on small(er) datasets

How to tune Pytorch Lightning hyperparameters by Richard

Common bugs: Tensorboard not showing in Jupyter-notebook see issue 79.; PyTorch 1.1.0 vs 1.2.0 support see FAQ; Bug <!-- A clear and concise description of what the bug is. --> Early stopping does not have the desired effect when creating a custom callback TorchMetrics documentation. TorchMetrics is a collection of Machine learning metrics for distributed, scalable PyTorch models and an easy-to-use API to create custom metrics. It offers the following benefits: You can use TorchMetrics in any PyTorch model, or with in PyTorch Lightning to enjoy additional features: This means that your data will. Use conda to check PyTorch package version. Similar to pip, if you used Anaconda to install PyTorch. you can use the command conda list to check its detail which also include the version info. conda list -f pytorch. You you want to check in another environment, e.g., pytorch14 below, use -n like this: conda list -n pytorch14 -f pytorch Image By Author. In a recent collaboration with Facebook AI's FairScale team and PyTorch Lightning, we're bringing you 50% memory reduction across all your models.Our goal at PyTorch Lightning is to make recent advancements in the field accessible to all researchers, especially when it comes to performance optimizations. Together with the FairScale team, we're excited to introduce our.

GitHub - PyTorchLightning/pytorch-lightning: The

# pytorch_lightningのインストール !pip install pytorch_lightning # pytorch関係 mport torch, torchvision import torch.nn as nn import torch.nn.functional as F from torchvision import transforms import pytorch_lightning as pl from pytorch_lightning import Trainer # フォルダ内のデータ読込で使用 from PIL import Image import glob そして、画像パスを配列に格納し. Last time I wrote about training the language models from scratch, you can find this post here. Now it's time to take your pre-trained lamnguage model at put it into good use by fine-tuning it for real world problem, i.e text classification or sentiment analysis. In this post I will show how to take pre-trained language model and build custom classifier on top of it Autologging is known to be compatible with the following package versions: 1.0.5 <= pytorch-lightning <= 1.3.0. Autologging may not succeed when used with package versions outside of this range. Enables (or disables) and configures autologging from PyTorch Lightning to MLflow. Autologging is performed when you call the fit method of pytorch.

python - Unable to import pytorch_lightning on google

PKGBUILD - aur.git - AUR Package Repositories. AUR : python-pytorch-lightning.git. AUR Package Repositories | click here to return to the package base details page. summary log tree commit diff stats. log msg author committer range. path: root / PKGBUILD

NVIDIA-Jetson-Xavier-NX | STRONGRAY

Pytorch Lightning set up on Jetson Nano/Xavier NX - Jetson

PyTorch Lightning 1lightning-bolts · PyPI让PyTorch更轻便,这款深度学习框架你值得拥有!在GitHub上斩获6
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