A CLI and library for interacting with the Weights and Biases API.
Project description
# Weights and Biases
[![ci](https://circleci.com/gh/wandb/client.svg?style=svg)](https://circleci.com/gh/wandb/client) [![pypi](https://img.shields.io/pypi/v/wandb.svg)](https://pypi.python.org/pypi/wandb) [![coveralls](https://coveralls.io/repos/github/wandb/client/badge.svg?branch=master)](https://coveralls.io/github/wandb/client?branch=master)
A CLI and library for interacting with the Weights and Biases API. Sign up for an account at [wandb.ai](https://wandb.ai)
## Features
* Keep a history of your weights and models from every training run
* Store all configuration parameters used in a training run
* Associate version control with your training runs
* Search and visualize training runs in a project
* Sync canonical models in your preferred format
## Usage
### CLI:
```shell
cd myproject
# Initialize a directory
wandb init
# Push files to W&B
wandb push bucket model.json weights.h5
# Pull files from canonical models
wandb pull zoo/inception-v4
# Sync training logs and push files when they change
./my_training.py | wandb bucket model.json weights.h5
# Manage configuration
wandb config set epochs=30
```
### Client:
```python
import wandb
conf = wandb.Config()
client = wandb.Api()
if conf.turbo:
print("TURBO MODE!!!")
client.push("my_bucket", files=["weights.h5", "model.json"])
```
Detailed usage can be found in our [documentation](http://wb-client.readthedocs.io/en/latest/usage.html).
[![ci](https://circleci.com/gh/wandb/client.svg?style=svg)](https://circleci.com/gh/wandb/client) [![pypi](https://img.shields.io/pypi/v/wandb.svg)](https://pypi.python.org/pypi/wandb) [![coveralls](https://coveralls.io/repos/github/wandb/client/badge.svg?branch=master)](https://coveralls.io/github/wandb/client?branch=master)
A CLI and library for interacting with the Weights and Biases API. Sign up for an account at [wandb.ai](https://wandb.ai)
## Features
* Keep a history of your weights and models from every training run
* Store all configuration parameters used in a training run
* Associate version control with your training runs
* Search and visualize training runs in a project
* Sync canonical models in your preferred format
## Usage
### CLI:
```shell
cd myproject
# Initialize a directory
wandb init
# Push files to W&B
wandb push bucket model.json weights.h5
# Pull files from canonical models
wandb pull zoo/inception-v4
# Sync training logs and push files when they change
./my_training.py | wandb bucket model.json weights.h5
# Manage configuration
wandb config set epochs=30
```
### Client:
```python
import wandb
conf = wandb.Config()
client = wandb.Api()
if conf.turbo:
print("TURBO MODE!!!")
client.push("my_bucket", files=["weights.h5", "model.json"])
```
Detailed usage can be found in our [documentation](http://wb-client.readthedocs.io/en/latest/usage.html).
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