| Name | wandb-zc JSON |
| Version |
0.17.7.dev1
JSON |
| download |
| home_page | None |
| Summary | A CLI and library for interacting with the Weights & Biases API. |
| upload_time | 2024-08-13 01:43:14 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2021 Weights and Biases, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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No coveralls.
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<div align="center">
<img src="https://i.imgur.com/RUtiVzH.png" width="600" /><br><br>
</div>
# Weights and Biases [](https://pypi.python.org/pypi/wandb) [](https://anaconda.org/conda-forge/wandb) [](https://circleci.com/gh/wandb/wandb) [](https://codecov.io/gh/wandb/wandb)
Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models.
- Quickly identify model regressions. Use W&B to visualize results in real time, all in a central dashboard.
- Focus on the interesting ML. Spend less time manually tracking results in spreadsheets and text files.
- Capture dataset versions with W&B Artifacts to identify how changing data affects your resulting models.
- Reproduce any model, with saved code, hyperparameters, launch commands, input data, and resulting model weights.
[Sign up for a free account →](https://wandb.ai/login?signup=true)
## Features
- Store hyper-parameters used in a training run
- Search, compare, and visualize training runs
- Analyze system usage metrics alongside runs
- Collaborate with team members
- Replicate historic results
- Run parameter sweeps
- Keep records of experiments available forever
[Documentation →](https://docs.wandb.com)
## Quickstart
```shell
pip install wandb
```
In your training script:
```python
import wandb
# Your custom arguments defined here
args = ...
wandb.init(config=args, project="my-project")
wandb.config["more"] = "custom"
def training_loop():
while True:
# Do some machine learning
epoch, loss, val_loss = ...
# Framework agnostic / custom metrics
wandb.log({"epoch": epoch, "loss": loss, "val_loss": val_loss})
```
If you're already using Tensorboard or [TensorboardX](https://github.com/lanpa/tensorboardX), you can integrate with one line:
```python
wandb.init(sync_tensorboard=True)
```
## Running your script
Run `wandb login` from your terminal to signup or authenticate your machine (we store your api key in ~/.netrc). You can also set the `WANDB_API_KEY` environment variable with a key from your [settings](https://app.wandb.ai/settings).
Run your script with `python my_script.py` and all metadata will be synced to the cloud. You will see a url in your terminal logs when your script starts and finishes. Data is staged locally in a directory named _wandb_ relative to your script. If you want to test your script without syncing to the cloud you can set the environment variable `WANDB_MODE=dryrun`.
If you are using [docker](https://docker.com) to run your code, we provide a wrapper command `wandb docker` that mounts your current directory, sets environment variables, and ensures the wandb library is installed. Training your models in docker gives you the ability to restore the exact code and environment with the `wandb restore` command.
## Web Interface
[Sign up for a free account →](https://wandb.com)
[](https://youtu.be/EeqhOSvNX-A)
[Introduction video →](https://youtu.be/EeqhOSvNX-A)
## Detailed Usage
Framework specific and detailed usage can be found in our [documentation](http://docs.wandb.com/).
## Testing
To run basic test use `make test`. More detailed information can be found at CONTRIBUTING.md.
We use [circleci](https://circleci.com) for CI.
# Academic Researchers
If you'd like a free academic account for your research group, [reach out to us →](https://www.wandb.com/academic)
We make it easy to cite W&B in your published paper. [Learn more →](https://www.wandb.com/academic)
[](https://www.wandb.com/academic)
## Community
Got questions, feedback or want to join a community of ML engineers working on exciting projects?
<a href="https://bit.ly/wb-slack"><img src="https://svgshare.com/i/M93.svg" alt="slack" width="55"/></a> Join our [slack](https://bit.ly/wb-slack) community.
[](https://twitter.com/weights_biases) Follow us on [Twitter](https://twitter.com/weights_biases).
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"description": "<div align=\"center\">\n <img src=\"https://i.imgur.com/RUtiVzH.png\" width=\"600\" /><br><br>\n</div>\n\n# Weights and Biases [](https://pypi.python.org/pypi/wandb) [](https://anaconda.org/conda-forge/wandb) [](https://circleci.com/gh/wandb/wandb) [](https://codecov.io/gh/wandb/wandb)\n\nUse W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models.\n\n- Quickly identify model regressions. Use W&B to visualize results in real time, all in a central dashboard.\n- Focus on the interesting ML. Spend less time manually tracking results in spreadsheets and text files.\n- Capture dataset versions with W&B Artifacts to identify how changing data affects your resulting models.\n- Reproduce any model, with saved code, hyperparameters, launch commands, input data, and resulting model weights.\n\n[Sign up for a free account \u2192](https://wandb.ai/login?signup=true)\n\n## Features\n\n- Store hyper-parameters used in a training run\n- Search, compare, and visualize training runs\n- Analyze system usage metrics alongside runs\n- Collaborate with team members\n- Replicate historic results\n- Run parameter sweeps\n- Keep records of experiments available forever\n\n[Documentation \u2192](https://docs.wandb.com)\n\n## Quickstart\n\n```shell\npip install wandb\n```\n\nIn your training script:\n\n```python\nimport wandb\n\n# Your custom arguments defined here\nargs = ...\n\nwandb.init(config=args, project=\"my-project\")\nwandb.config[\"more\"] = \"custom\"\n\n\ndef training_loop():\n while True:\n # Do some machine learning\n epoch, loss, val_loss = ...\n # Framework agnostic / custom metrics\n wandb.log({\"epoch\": epoch, \"loss\": loss, \"val_loss\": val_loss})\n```\n\nIf you're already using Tensorboard or [TensorboardX](https://github.com/lanpa/tensorboardX), you can integrate with one line:\n\n```python\nwandb.init(sync_tensorboard=True)\n```\n\n## Running your script\n\nRun `wandb login` from your terminal to signup or authenticate your machine (we store your api key in ~/.netrc). You can also set the `WANDB_API_KEY` environment variable with a key from your [settings](https://app.wandb.ai/settings).\n\nRun your script with `python my_script.py` and all metadata will be synced to the cloud. You will see a url in your terminal logs when your script starts and finishes. Data is staged locally in a directory named _wandb_ relative to your script. If you want to test your script without syncing to the cloud you can set the environment variable `WANDB_MODE=dryrun`.\n\nIf you are using [docker](https://docker.com) to run your code, we provide a wrapper command `wandb docker` that mounts your current directory, sets environment variables, and ensures the wandb library is installed. Training your models in docker gives you the ability to restore the exact code and environment with the `wandb restore` command.\n\n## Web Interface\n\n[Sign up for a free account \u2192](https://wandb.com)\n[](https://youtu.be/EeqhOSvNX-A)\n[Introduction video \u2192](https://youtu.be/EeqhOSvNX-A)\n\n## Detailed Usage\n\nFramework specific and detailed usage can be found in our [documentation](http://docs.wandb.com/).\n\n## Testing\n\nTo run basic test use `make test`. More detailed information can be found at CONTRIBUTING.md.\n\nWe use [circleci](https://circleci.com) for CI.\n\n# Academic Researchers\nIf you'd like a free academic account for your research group, [reach out to us \u2192](https://www.wandb.com/academic)\n\nWe make it easy to cite W&B in your published paper. [Learn more \u2192](https://www.wandb.com/academic)\n[](https://www.wandb.com/academic)\n\n## Community\nGot questions, feedback or want to join a community of ML engineers working on exciting projects?\n\n<a href=\"https://bit.ly/wb-slack\"><img src=\"https://svgshare.com/i/M93.svg\" alt=\"slack\" width=\"55\"/></a> Join our [slack](https://bit.ly/wb-slack) community.\n\n[](https://twitter.com/weights_biases) Follow us on [Twitter](https://twitter.com/weights_biases).\n",
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