Python库 | test_tube-0.6.7.1.tar.gz

<p align="center"> <a href="https://williamfalcon.github.io/test-tube/"> <img alt="react-router" src="https://raw.githubusercontent.com/williamfalcon/test-tube/master/imgs/test_tube_logo.png" width="50"> </a> </p> <h3 align="center"> Test Tube </h3> <p align="center"> Log, organize and parallelize hyperparameter search for Deep Learning experiments </p> <p align="center"> <a href="https://badge.fury.io/py/test-tube"><img src="https://badge.fury.io/py/test-tube.svg" alt="PyPI version" height="18"></a> <a href="https://travis-ci.org/williamFalcon/test-tube"><img src="https://travis-ci.org/williamFalcon/test-tube.svg?branch=master"></a> <a href="https://williamfalcon.github.io/test-tube/"><img src="https://readthedocs.org/projects/test-tube/badge/?version=latest"></a> <a href="https://github.com/williamFalcon/test-tube/blob/master/LICENSE"><img src="https://img.shields.io/badge/License-MIT-yellow.svg"></a> </p> ## Docs **[View the docs here](https://williamfalcon.github.io/test-tube/)** --- Test tube is a python library to track and parallelize hyperparameter search for Deep Learning and ML experiments. It's framework agnostic and built on top of the python argparse API for ease of use. ``` {.bash} pip install test_tube ``` --- ### Main test-tube uses - [Parallelize hyperparameter optimization](https://williamfalcon.github.io/test-tube/hyperparameter_optimization/HyperOptArgumentParser/) (across multiple gpus or cpus). - [Parallelize hyperparameter optimization](https://williamfalcon.github.io/test-tube/hyperparameter_optimization/HyperOptArgumentParser/) across HPC cluster using SLURM. - Log experiment hyperparameters and experiment data. [Experiments](https://williamfalcon.github.io/test-tube/experiment_tracking/experiment/) across models. - Visualize with [tensorboard](https://www.tensorflow.org/guide/summaries_and_tensorboard) Compatible with Python any Python ML library like Tensorflow, Keras, Pytorch, Caffe, Caffe2, Chainer, MXNet, Theano, Scikit-learn --- ### Examples The Experiment object is a subclass of Pytorch.SummaryWriter. **Log and visualize with Tensorboard** ```{.python} from test-tube import Experiment import torch exp = Experiment('/some/path') exp.tag({'learning_rate': 0.02, 'layers': 4}) # exp is superclass of SummaryWriter features = torch.Tensor(100, 784) writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1)) # simulate training for n_iter in range(2000): e.log({'testtt': n_iter * np.sin(n_iter)}) # save and close exp.save() exp.close() ``` ```{.bash} pip install tensorflow tensorboard --logdir /some/path ``` **Run grid search on SLURM GPU cluster** ``` {.python} from test_tube.hpc import SlurmCluster # hyperparameters is a test-tube hyper params object hyperparams = args.parse() # init cluster cluster = SlurmCluster( hyperparam_optimizer=hyperparams, log_path='/path/to/log/results/to', python_cmd='python3' ) # let the cluster know where to email for a change in job status (ie: complete, fail, etc...) cluster.notify_job_status(email='some@email.com', on_done=True, on_fail=True) # set the job options. In this instance, we'll run 20 different models # each with its own set of hyperparameters giving each one 1 GPU (ie: taking up 20 GPUs) cluster.per_experiment_nb_gpus = 1 cluster.per_experiment_nb_nodes = 1 # run the models on the cluster cluster.optimize_parallel_cluster_gpu(train, nb_trials=20, job_name='first_tt_batch', job_display_name='my_batch') # we just ran 20 different hyperparameters on 20 GPUs in the HPC cluster!! ``` **Optimize hyperparameters across GPUs** ``` {.python} from test_tube import HyperOptArgumentParser # subclass of argparse parser = HyperOptArgumentParser(strategy='random_search') parser.add_argument('--learning_rate', default=0.002, type=float, help='the learning rate') # let's enable optimizing over the number of layers in the network parser.opt_list('--nb_layers', default=2, type=int, tunable=True, options=[2, 4, 8]) # and tune the number of units in each layer parser.opt_range('--neurons', default=50, type=int, tunable=True, low=100, high=800, nb_samples=10) # compile (because it's argparse underneath) hparams = parser.parse_args() # optimize across 4 gpus # use 2 gpus together and the other two separately hparams.optimize_parallel_gpu(MyModel.fit, gpu_ids=['1', '2,3', '0'], nb_trials=192, nb_workers=4) ``` Or... across CPUs ``` {.python} hparams.optimize_parallel_cpu(MyModel.fit, nb_trials=192, nb_workers=12) ``` You can also optimize on a *log* scale to allow better search over magnitudes of hyperparameter values, with a chosen base (disabled by default). Keep in mind that the range you search over must be strictly positive. ``` {.python} from test_tube import HyperOptArgumentParser # subclass of argparse parser = HyperOptArgumentParser(strategy='random_search') # Randomly searches over the (log-transformed) range [100,800). parser.opt_range('--neurons', default=50, type=int, tunable=True, low=100, high=800, nb_samples=10, log_base=10) # compile (because it's argparse underneath) hparams = parser.parse_args() # run 20 trials of random search over the hyperparams for hparam_trial in hparams.trials(20): train_network(hparam_trial) ``` ### Convert your argparse params into searchable params by changing 1 line ``` {.python} import argparse from test_tube import HyperOptArgumentParser # these lines are equivalent parser = argparse.ArgumentParser(description='Process some integers.') parser = HyperOptArgumentParser(description='Process some integers.', strategy='grid_search') # do normal argparse stuff ... ``` ### Log images inline with metrics ``` {.python} # name must have either jpg, png or jpeg in it img = np.imread('a.jpg') exp.log('test_jpg': img, 'val_err': 0.2) # saves image to ../exp/version/media/test_0.jpg # csv has file path to that image in that cell ``` ## Demos - [Hyperparameter optimization for PyTorch across 20 cluster GPUs](https://github.com/williamFalcon/test-tube/blob/master/examples/pytorch_hpc_example.py) - [Hyperparameter optimization across 20 cluster CPUs](https://github.com/williamFalcon/test-tube/blob/master/examples/hpc_cpu_example.py) - [Experiments and hyperparameter optimization for tensorflow across 4 GPUs simultaneously](https://github.com/williamFalcon/test-tube/blob/master/examples/tensorflow_example.py) ## How to contribute Feel free to fix bugs and make improvements! 1. Check out the [current bugs here](https://github.com/williamFalcon/test-tube/issues) or [feature requests](https://github.com/williamFalcon/test-tube/projects/1). 2. To work on a bug or feature, head over to our [project page](https://github.com/williamFalcon/test-tube/projects/1) and assign yourself the bug. 3. We'll add contributor names periodically as people improve the library! ## Bibtex To cite the framework use: @misc{Falcon2017, author = {Falcon, W.A.}, title = {Test Tube}, year = {2017}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/williamfalcon/test-tube}} } ## License In addition to the terms outlined in the license, this software is U.S. Patent Pending.