%global _empty_manifest_terminate_build 0
Name: python-wandb
Version: 0.14.2
Release: 1
Summary: A CLI and library for interacting with the Weights and Biases API.
License: MIT license
URL: https://github.com/wandb/wandb
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/27/8c/450c329e607e7b09413e27a5953adbe0ee61071fb54370fa3e4424fec9ed/wandb-0.14.2.tar.gz
BuildArch: noarch
Requires: python3-Click
Requires: python3-GitPython
Requires: python3-requests
Requires: python3-psutil
Requires: python3-sentry-sdk
Requires: python3-docker-pycreds
Requires: python3-PyYAML
Requires: python3-pathtools
Requires: python3-setproctitle
Requires: python3-setuptools
Requires: python3-appdirs
Requires: python3-typing-extensions
Requires: python3-dataclasses
Requires: python3-protobuf
Requires: python3-protobuf
Requires: python3-protobuf
Requires: python3-protobuf
Requires: python3-httpx
Requires: python3-boto3
Requires: python3-azure-storage-blob
Requires: python3-google-cloud-storage
Requires: python3-grpcio
Requires: python3-kubernetes
Requires: python3-minio
Requires: python3-google-cloud-storage
Requires: python3-sh
Requires: python3-awscli
Requires: python3-nbconvert
Requires: python3-nbformat
Requires: python3-chardet
Requires: python3-iso8601
Requires: python3-typing-extensions
Requires: python3-boto3
Requires: python3-botocore
Requires: python3-google-auth
Requires: python3-google-cloud-compute
Requires: python3-google-cloud-storage
Requires: python3-google-cloud-artifact-registry
Requires: python3-kubernetes
Requires: python3-numpy
Requires: python3-moviepy
Requires: python3-pillow
Requires: python3-bokeh
Requires: python3-soundfile
Requires: python3-plotly
Requires: python3-rdkit-pypi
Requires: python3-cloudpickle
Requires: python3-sweeps
%description
# 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?
Join our [slack](https://bit.ly/wb-slack) community.
[](https://twitter.com/weights_biases) Follow us on [Twitter](https://twitter.com/weights_biases).
%package -n python3-wandb
Summary: A CLI and library for interacting with the Weights and Biases API.
Provides: python-wandb
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-wandb
# 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?
Join our [slack](https://bit.ly/wb-slack) community.
[](https://twitter.com/weights_biases) Follow us on [Twitter](https://twitter.com/weights_biases).
%package help
Summary: Development documents and examples for wandb
Provides: python3-wandb-doc
%description help
# 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?
Join our [slack](https://bit.ly/wb-slack) community.
[](https://twitter.com/weights_biases) Follow us on [Twitter](https://twitter.com/weights_biases).
%prep
%autosetup -n wandb-0.14.2
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-wandb -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot - 0.14.2-1
- Package Spec generated
