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@@ -0,0 +1 @@ +/ai2-tango-1.2.1.tar.gz diff --git a/python-ai2-tango.spec b/python-ai2-tango.spec new file mode 100644 index 0000000..709fc47 --- /dev/null +++ b/python-ai2-tango.spec @@ -0,0 +1,965 @@ +%global _empty_manifest_terminate_build 0 +Name: python-ai2-tango +Version: 1.2.1 +Release: 1 +Summary: A library for choreographing your machine learning research. +License: Apache +URL: https://github.com/allenai/tango +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f0/0d/12f9f4b125aeff7c4dc54e25c671afaaf63748a74109a460c524493d730f/ai2-tango-1.2.1.tar.gz +BuildArch: noarch + +Requires: python3-cached-path +Requires: python3-rjsonnet +Requires: python3-GitPython +Requires: python3-PyYAML +Requires: python3-dill +Requires: python3-base58 +Requires: python3-xxhash +Requires: python3-filelock +Requires: python3-click +Requires: python3-click-help-colors +Requires: python3-rich +Requires: python3-tqdm +Requires: python3-more-itertools +Requires: python3-sqlitedict +Requires: python3-glob2 +Requires: python3-petname +Requires: python3-pytz +Requires: python3-torch +Requires: python3-numpy +Requires: python3-datasets +Requires: python3-wandb +Requires: python3-retry +Requires: python3-transformers +Requires: python3-sentencepiece +Requires: python3-fairscale +Requires: python3-beaker-py +Requires: python3-google-cloud-storage +Requires: python3-google-cloud-datastore +Requires: python3-sacremoses +Requires: python3-jax +Requires: python3-flax +Requires: python3-optax +Requires: python3-tensorflow-cpu +Requires: python3-beaker-py +Requires: python3-datasets +Requires: python3-flake8 +Requires: python3-mypy +Requires: python3-types-PyYAML +Requires: python3-types-setuptools +Requires: python3-types-pytz +Requires: python3-types-retry +Requires: python3-black +Requires: python3-isort +Requires: python3-pytest +Requires: python3-pytest-sphinx +Requires: python3-flaky +Requires: python3-pytest-cov +Requires: python3-coverage +Requires: python3-codecov +Requires: python3-twine +Requires: python3-setuptools +Requires: python3-wheel +Requires: python3-Sphinx +Requires: python3-furo +Requires: python3-myst-parser +Requires: python3-sphinx-copybutton +Requires: python3-sphinx-autobuild +Requires: python3-sphinx-autodoc-typehints +Requires: python3-packaging +Requires: python3-torchmetrics +Requires: python3-torch +Requires: python3-numpy +Requires: python3-fairscale +Requires: python3-datasets +Requires: python3-jax +Requires: python3-flax +Requires: python3-optax +Requires: python3-tensorflow-cpu +Requires: python3-google-cloud-storage +Requires: python3-google-cloud-datastore +Requires: python3-torch +Requires: python3-numpy +Requires: python3-torch +Requires: python3-numpy +Requires: python3-datasets +Requires: python3-transformers +Requires: python3-sentencepiece +Requires: python3-sacremoses +Requires: python3-wandb +Requires: python3-retry + +%description +<div align="center"> +<br> +<img src="https://raw.githubusercontent.com/allenai/tango/main/docs/source/_static/tango_final_horizontal.png" width="600"/> +<br> +<br> +<p> +<!-- start tagline --> +AI2 Tango replaces messy directories and spreadsheets full of file versions by organizing experiments into discrete steps that can be cached and reused throughout the lifetime of a research project. +<!-- end tagline --> +</p> +<hr/> +<a href="https://github.com/allenai/tango/actions"> + <img alt="CI" src="https://github.com/allenai/tango/workflows/CI/badge.svg?event=push&branch=main"> +</a> +<a href="https://pypi.org/project/ai2-tango/"> + <img alt="PyPI" src="https://img.shields.io/pypi/v/ai2-tango"> +</a> +<a href="https://ai2-tango.readthedocs.io/en/latest/?badge=latest"> + <img src="https://readthedocs.org/projects/ai2-tango/badge/?version=latest" alt="Documentation Status" /> +</a> +<a href="https://github.com/allenai/tango/blob/main/LICENSE"> + <img alt="License" src="https://img.shields.io/github/license/allenai/tango.svg?color=blue&cachedrop"> +</a> +<br/> +</div> + +## Quick links + +- [Documentation](https://ai2-tango.readthedocs.io/) +- [PyPI Package](https://pypi.org/project/ai2-tango/) +- [Contributing](https://github.com/allenai/tango/blob/main/CONTRIBUTING.md) +- [License](https://github.com/allenai/tango/blob/main/LICENSE) + +## In this README + +- [Quick start](#quick-start) +- [Installation](#installation) + - [Installing with PIP](#installing-with-pip) + - [Installing with Conda](#installing-with-conda) + - [Installing from source](#installing-from-source) + - [Checking your installation](#checking-your-installation) + - [Docker image](#docker-image) +- [FAQ](#faq) +- [Team](#team) +- [License](#license) + +## Quick start + +Create a Tango step: + +```python +# hello.py + +from tango import step + +@step() +def hello(name: str) -> str: + message = f"Hello, {name}!" + print(message) + return message +``` + +And create a corresponding experiment configuration file: + +```jsonnet +// hello.jsonnet + +{ + steps: { + hello: { + type: "hello", + name: "World", + } + } +} +``` + +Then run the experiment using a local workspace to cache the result: + +```bash +tango run hello.jsonnet -w /tmp/workspace +``` + +You'll see something like this in the output: + +``` +Starting new run expert-llama +● Starting step "hello"... +Hello, World! +✓ Finished step "hello" +✓ Finished run expert-llama +``` + +If you run this a second time the output will now look like this: + +``` +Starting new run open-crab +✓ Found output for step "hello" in cache... +✓ Finished run open-crab +``` + +You won't see "Hello, World!" this time because the result of the step was found in the cache, so it wasn't run again. + +For a more detailed introduction check out the [First Steps](https://ai2-tango.readthedocs.io/en/latest/first_steps.html) walk-through. + +## Installation + +<!-- start install --> + +**ai2-tango** requires Python 3.8 or later. + +### Installing with `pip` + +**ai2-tango** is available [on PyPI](https://pypi.org/project/ai2-tango/). Just run + +```bash +pip install ai2-tango +``` + +To install with a specific integration, such as `torch` for example, run + +```bash +pip install 'ai2-tango[torch]' +``` + +To install with all integrations, run + +```bash +pip install 'ai2-tango[all]' +``` + +### Installing with `conda` + +**ai2-tango** is available on conda-forge. You can install just the base package with + +```bash +conda install tango -c conda-forge +``` + +You can pick and choose from the integrations with one of these: + +```bash +conda install tango-datasets -c conda-forge +conda install tango-torch -c conda-forge +conda install tango-wandb -c conda-forge +``` + +You can also install everything: + +```bash +conda install tango-all -c conda-forge +``` + +Even though **ai2-tango** itself is quite small, installing everything will pull in a lot of dependencies. +Don't be surprised if this takes a while! + +### Installing from source + +To install **ai2-tango** from source, first clone [the repository](https://github.com/allenai/tango): + +```bash +git clone https://github.com/allenai/tango.git +cd tango +``` + +Then run + +```bash +pip install -e '.[all]' +``` + +To install with only a specific integration, such as `torch` for example, run + +```bash +pip install -e '.[torch]' +``` + +Or to install just the base tango library, you can run + +```bash +pip install -e . +``` + +### Checking your installation + +Run + +```bash +tango info +``` + +to check your installation. + +### Docker image + +You can build a Docker image suitable for tango projects by using [the official Dockerfile](https://github.com/allenai/tango/blob/main/Dockerfile) as a starting point for your own Dockerfile, or you can simply use one of our [prebuilt images](https://github.com/allenai/tango/pkgs/container/tango) as a base image in your Dockerfile. For example: + +```Dockerfile +# Start from a prebuilt tango base image. +# You can choose the right tag from the available options here: +# https://github.com/allenai/tango/pkgs/container/tango/versions +FROM ghcr.io/allenai/tango:cuda11.3 + +# Install your project's additional requirements. +COPY requirements.txt . +RUN /opt/conda/bin/pip install --no-cache-dir -r requirements.txt + +# Install source code. +# This instruction copies EVERYTHING in the current directory (build context), +# which may not be what you want. Consider using a ".dockerignore" file to +# exclude files and directories that you don't want on the image. +COPY . . +``` + +Make sure to choose the right base image for your use case depending on the version of tango you're using and the CUDA version that your host machine supports. +You can see a list of all available image tags [on GitHub](https://github.com/allenai/tango/pkgs/container/tango/versions). + +<!-- end install --> + +## FAQ + +<!-- start faq --> + +### Why is the library named Tango? + +The motivation behind this library is that we can make research easier by composing it into well-defined steps. What happens when you choreograph a number of steps together? Well, you get a dance. And since our [team's leader](https://nasmith.github.io/) is part of a tango band, "AI2 Tango" was an obvious choice! + +### How can I debug my steps through the Tango CLI? + +You can run the `tango` command through [pdb](https://docs.python.org/3/library/pdb.html). For example: + +```bash +python -m pdb -m tango run config.jsonnet +``` + +### How is Tango different from [Metaflow](https://metaflow.org), [Airflow](https://airflow.apache.org), or [redun](https://github.com/insitro/redun)? + +We've found that existing DAG execution engines like these tools are great for production workflows but not as well suited for messy, collaborative research projects +where code is changing constantly. AI2 Tango was built *specifically* for these kinds of research projects. + +### How does Tango's caching mechanism work? + +AI2 Tango caches the results of steps based on the `unique_id` of the step. The `unique_id` is essentially a hash of all of the inputs to the step along with: + +1. the step class's fully qualified name, and +2. the step class's `VERSION` class variable (an arbitrary string). + +Unlike other workflow engines like [redun](https://github.com/insitro/redun), Tango does *not* take into account the source code of the class itself (other than its fully qualified name) because we've found that using a hash of the source code bytes is way too sensitive and less transparent for users. +When you change the source code of your step in a meaningful way you can just manually change the `VERSION` class variable to indicate to Tango +that the step has been updated. + +<!-- end faq --> + +## Team + +<!-- start team --> + +**ai2-tango** is developed and maintained by the AllenNLP team, backed by [the Allen Institute for Artificial Intelligence (AI2)](https://allenai.org/). +AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering. +To learn more about who specifically contributed to this codebase, see [our contributors](https://github.com/allenai/tango/graphs/contributors) page. + +<!-- end team --> + +## License + +<!-- start license --> + +**ai2-tango** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). +A full copy of the license can be found [on GitHub](https://github.com/allenai/tango/blob/main/LICENSE). + +<!-- end license --> + + +%package -n python3-ai2-tango +Summary: A library for choreographing your machine learning research. +Provides: python-ai2-tango +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-ai2-tango +<div align="center"> +<br> +<img src="https://raw.githubusercontent.com/allenai/tango/main/docs/source/_static/tango_final_horizontal.png" width="600"/> +<br> +<br> +<p> +<!-- start tagline --> +AI2 Tango replaces messy directories and spreadsheets full of file versions by organizing experiments into discrete steps that can be cached and reused throughout the lifetime of a research project. +<!-- end tagline --> +</p> +<hr/> +<a href="https://github.com/allenai/tango/actions"> + <img alt="CI" src="https://github.com/allenai/tango/workflows/CI/badge.svg?event=push&branch=main"> +</a> +<a href="https://pypi.org/project/ai2-tango/"> + <img alt="PyPI" src="https://img.shields.io/pypi/v/ai2-tango"> +</a> +<a href="https://ai2-tango.readthedocs.io/en/latest/?badge=latest"> + <img src="https://readthedocs.org/projects/ai2-tango/badge/?version=latest" alt="Documentation Status" /> +</a> +<a href="https://github.com/allenai/tango/blob/main/LICENSE"> + <img alt="License" src="https://img.shields.io/github/license/allenai/tango.svg?color=blue&cachedrop"> +</a> +<br/> +</div> + +## Quick links + +- [Documentation](https://ai2-tango.readthedocs.io/) +- [PyPI Package](https://pypi.org/project/ai2-tango/) +- [Contributing](https://github.com/allenai/tango/blob/main/CONTRIBUTING.md) +- [License](https://github.com/allenai/tango/blob/main/LICENSE) + +## In this README + +- [Quick start](#quick-start) +- [Installation](#installation) + - [Installing with PIP](#installing-with-pip) + - [Installing with Conda](#installing-with-conda) + - [Installing from source](#installing-from-source) + - [Checking your installation](#checking-your-installation) + - [Docker image](#docker-image) +- [FAQ](#faq) +- [Team](#team) +- [License](#license) + +## Quick start + +Create a Tango step: + +```python +# hello.py + +from tango import step + +@step() +def hello(name: str) -> str: + message = f"Hello, {name}!" + print(message) + return message +``` + +And create a corresponding experiment configuration file: + +```jsonnet +// hello.jsonnet + +{ + steps: { + hello: { + type: "hello", + name: "World", + } + } +} +``` + +Then run the experiment using a local workspace to cache the result: + +```bash +tango run hello.jsonnet -w /tmp/workspace +``` + +You'll see something like this in the output: + +``` +Starting new run expert-llama +● Starting step "hello"... +Hello, World! +✓ Finished step "hello" +✓ Finished run expert-llama +``` + +If you run this a second time the output will now look like this: + +``` +Starting new run open-crab +✓ Found output for step "hello" in cache... +✓ Finished run open-crab +``` + +You won't see "Hello, World!" this time because the result of the step was found in the cache, so it wasn't run again. + +For a more detailed introduction check out the [First Steps](https://ai2-tango.readthedocs.io/en/latest/first_steps.html) walk-through. + +## Installation + +<!-- start install --> + +**ai2-tango** requires Python 3.8 or later. + +### Installing with `pip` + +**ai2-tango** is available [on PyPI](https://pypi.org/project/ai2-tango/). Just run + +```bash +pip install ai2-tango +``` + +To install with a specific integration, such as `torch` for example, run + +```bash +pip install 'ai2-tango[torch]' +``` + +To install with all integrations, run + +```bash +pip install 'ai2-tango[all]' +``` + +### Installing with `conda` + +**ai2-tango** is available on conda-forge. You can install just the base package with + +```bash +conda install tango -c conda-forge +``` + +You can pick and choose from the integrations with one of these: + +```bash +conda install tango-datasets -c conda-forge +conda install tango-torch -c conda-forge +conda install tango-wandb -c conda-forge +``` + +You can also install everything: + +```bash +conda install tango-all -c conda-forge +``` + +Even though **ai2-tango** itself is quite small, installing everything will pull in a lot of dependencies. +Don't be surprised if this takes a while! + +### Installing from source + +To install **ai2-tango** from source, first clone [the repository](https://github.com/allenai/tango): + +```bash +git clone https://github.com/allenai/tango.git +cd tango +``` + +Then run + +```bash +pip install -e '.[all]' +``` + +To install with only a specific integration, such as `torch` for example, run + +```bash +pip install -e '.[torch]' +``` + +Or to install just the base tango library, you can run + +```bash +pip install -e . +``` + +### Checking your installation + +Run + +```bash +tango info +``` + +to check your installation. + +### Docker image + +You can build a Docker image suitable for tango projects by using [the official Dockerfile](https://github.com/allenai/tango/blob/main/Dockerfile) as a starting point for your own Dockerfile, or you can simply use one of our [prebuilt images](https://github.com/allenai/tango/pkgs/container/tango) as a base image in your Dockerfile. For example: + +```Dockerfile +# Start from a prebuilt tango base image. +# You can choose the right tag from the available options here: +# https://github.com/allenai/tango/pkgs/container/tango/versions +FROM ghcr.io/allenai/tango:cuda11.3 + +# Install your project's additional requirements. +COPY requirements.txt . +RUN /opt/conda/bin/pip install --no-cache-dir -r requirements.txt + +# Install source code. +# This instruction copies EVERYTHING in the current directory (build context), +# which may not be what you want. Consider using a ".dockerignore" file to +# exclude files and directories that you don't want on the image. +COPY . . +``` + +Make sure to choose the right base image for your use case depending on the version of tango you're using and the CUDA version that your host machine supports. +You can see a list of all available image tags [on GitHub](https://github.com/allenai/tango/pkgs/container/tango/versions). + +<!-- end install --> + +## FAQ + +<!-- start faq --> + +### Why is the library named Tango? + +The motivation behind this library is that we can make research easier by composing it into well-defined steps. What happens when you choreograph a number of steps together? Well, you get a dance. And since our [team's leader](https://nasmith.github.io/) is part of a tango band, "AI2 Tango" was an obvious choice! + +### How can I debug my steps through the Tango CLI? + +You can run the `tango` command through [pdb](https://docs.python.org/3/library/pdb.html). For example: + +```bash +python -m pdb -m tango run config.jsonnet +``` + +### How is Tango different from [Metaflow](https://metaflow.org), [Airflow](https://airflow.apache.org), or [redun](https://github.com/insitro/redun)? + +We've found that existing DAG execution engines like these tools are great for production workflows but not as well suited for messy, collaborative research projects +where code is changing constantly. AI2 Tango was built *specifically* for these kinds of research projects. + +### How does Tango's caching mechanism work? + +AI2 Tango caches the results of steps based on the `unique_id` of the step. The `unique_id` is essentially a hash of all of the inputs to the step along with: + +1. the step class's fully qualified name, and +2. the step class's `VERSION` class variable (an arbitrary string). + +Unlike other workflow engines like [redun](https://github.com/insitro/redun), Tango does *not* take into account the source code of the class itself (other than its fully qualified name) because we've found that using a hash of the source code bytes is way too sensitive and less transparent for users. +When you change the source code of your step in a meaningful way you can just manually change the `VERSION` class variable to indicate to Tango +that the step has been updated. + +<!-- end faq --> + +## Team + +<!-- start team --> + +**ai2-tango** is developed and maintained by the AllenNLP team, backed by [the Allen Institute for Artificial Intelligence (AI2)](https://allenai.org/). +AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering. +To learn more about who specifically contributed to this codebase, see [our contributors](https://github.com/allenai/tango/graphs/contributors) page. + +<!-- end team --> + +## License + +<!-- start license --> + +**ai2-tango** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). +A full copy of the license can be found [on GitHub](https://github.com/allenai/tango/blob/main/LICENSE). + +<!-- end license --> + + +%package help +Summary: Development documents and examples for ai2-tango +Provides: python3-ai2-tango-doc +%description help +<div align="center"> +<br> +<img src="https://raw.githubusercontent.com/allenai/tango/main/docs/source/_static/tango_final_horizontal.png" width="600"/> +<br> +<br> +<p> +<!-- start tagline --> +AI2 Tango replaces messy directories and spreadsheets full of file versions by organizing experiments into discrete steps that can be cached and reused throughout the lifetime of a research project. +<!-- end tagline --> +</p> +<hr/> +<a href="https://github.com/allenai/tango/actions"> + <img alt="CI" src="https://github.com/allenai/tango/workflows/CI/badge.svg?event=push&branch=main"> +</a> +<a href="https://pypi.org/project/ai2-tango/"> + <img alt="PyPI" src="https://img.shields.io/pypi/v/ai2-tango"> +</a> +<a href="https://ai2-tango.readthedocs.io/en/latest/?badge=latest"> + <img src="https://readthedocs.org/projects/ai2-tango/badge/?version=latest" alt="Documentation Status" /> +</a> +<a href="https://github.com/allenai/tango/blob/main/LICENSE"> + <img alt="License" src="https://img.shields.io/github/license/allenai/tango.svg?color=blue&cachedrop"> +</a> +<br/> +</div> + +## Quick links + +- [Documentation](https://ai2-tango.readthedocs.io/) +- [PyPI Package](https://pypi.org/project/ai2-tango/) +- [Contributing](https://github.com/allenai/tango/blob/main/CONTRIBUTING.md) +- [License](https://github.com/allenai/tango/blob/main/LICENSE) + +## In this README + +- [Quick start](#quick-start) +- [Installation](#installation) + - [Installing with PIP](#installing-with-pip) + - [Installing with Conda](#installing-with-conda) + - [Installing from source](#installing-from-source) + - [Checking your installation](#checking-your-installation) + - [Docker image](#docker-image) +- [FAQ](#faq) +- [Team](#team) +- [License](#license) + +## Quick start + +Create a Tango step: + +```python +# hello.py + +from tango import step + +@step() +def hello(name: str) -> str: + message = f"Hello, {name}!" + print(message) + return message +``` + +And create a corresponding experiment configuration file: + +```jsonnet +// hello.jsonnet + +{ + steps: { + hello: { + type: "hello", + name: "World", + } + } +} +``` + +Then run the experiment using a local workspace to cache the result: + +```bash +tango run hello.jsonnet -w /tmp/workspace +``` + +You'll see something like this in the output: + +``` +Starting new run expert-llama +● Starting step "hello"... +Hello, World! +✓ Finished step "hello" +✓ Finished run expert-llama +``` + +If you run this a second time the output will now look like this: + +``` +Starting new run open-crab +✓ Found output for step "hello" in cache... +✓ Finished run open-crab +``` + +You won't see "Hello, World!" this time because the result of the step was found in the cache, so it wasn't run again. + +For a more detailed introduction check out the [First Steps](https://ai2-tango.readthedocs.io/en/latest/first_steps.html) walk-through. + +## Installation + +<!-- start install --> + +**ai2-tango** requires Python 3.8 or later. + +### Installing with `pip` + +**ai2-tango** is available [on PyPI](https://pypi.org/project/ai2-tango/). Just run + +```bash +pip install ai2-tango +``` + +To install with a specific integration, such as `torch` for example, run + +```bash +pip install 'ai2-tango[torch]' +``` + +To install with all integrations, run + +```bash +pip install 'ai2-tango[all]' +``` + +### Installing with `conda` + +**ai2-tango** is available on conda-forge. You can install just the base package with + +```bash +conda install tango -c conda-forge +``` + +You can pick and choose from the integrations with one of these: + +```bash +conda install tango-datasets -c conda-forge +conda install tango-torch -c conda-forge +conda install tango-wandb -c conda-forge +``` + +You can also install everything: + +```bash +conda install tango-all -c conda-forge +``` + +Even though **ai2-tango** itself is quite small, installing everything will pull in a lot of dependencies. +Don't be surprised if this takes a while! + +### Installing from source + +To install **ai2-tango** from source, first clone [the repository](https://github.com/allenai/tango): + +```bash +git clone https://github.com/allenai/tango.git +cd tango +``` + +Then run + +```bash +pip install -e '.[all]' +``` + +To install with only a specific integration, such as `torch` for example, run + +```bash +pip install -e '.[torch]' +``` + +Or to install just the base tango library, you can run + +```bash +pip install -e . +``` + +### Checking your installation + +Run + +```bash +tango info +``` + +to check your installation. + +### Docker image + +You can build a Docker image suitable for tango projects by using [the official Dockerfile](https://github.com/allenai/tango/blob/main/Dockerfile) as a starting point for your own Dockerfile, or you can simply use one of our [prebuilt images](https://github.com/allenai/tango/pkgs/container/tango) as a base image in your Dockerfile. For example: + +```Dockerfile +# Start from a prebuilt tango base image. +# You can choose the right tag from the available options here: +# https://github.com/allenai/tango/pkgs/container/tango/versions +FROM ghcr.io/allenai/tango:cuda11.3 + +# Install your project's additional requirements. +COPY requirements.txt . +RUN /opt/conda/bin/pip install --no-cache-dir -r requirements.txt + +# Install source code. +# This instruction copies EVERYTHING in the current directory (build context), +# which may not be what you want. Consider using a ".dockerignore" file to +# exclude files and directories that you don't want on the image. +COPY . . +``` + +Make sure to choose the right base image for your use case depending on the version of tango you're using and the CUDA version that your host machine supports. +You can see a list of all available image tags [on GitHub](https://github.com/allenai/tango/pkgs/container/tango/versions). + +<!-- end install --> + +## FAQ + +<!-- start faq --> + +### Why is the library named Tango? + +The motivation behind this library is that we can make research easier by composing it into well-defined steps. What happens when you choreograph a number of steps together? Well, you get a dance. And since our [team's leader](https://nasmith.github.io/) is part of a tango band, "AI2 Tango" was an obvious choice! + +### How can I debug my steps through the Tango CLI? + +You can run the `tango` command through [pdb](https://docs.python.org/3/library/pdb.html). For example: + +```bash +python -m pdb -m tango run config.jsonnet +``` + +### How is Tango different from [Metaflow](https://metaflow.org), [Airflow](https://airflow.apache.org), or [redun](https://github.com/insitro/redun)? + +We've found that existing DAG execution engines like these tools are great for production workflows but not as well suited for messy, collaborative research projects +where code is changing constantly. AI2 Tango was built *specifically* for these kinds of research projects. + +### How does Tango's caching mechanism work? + +AI2 Tango caches the results of steps based on the `unique_id` of the step. The `unique_id` is essentially a hash of all of the inputs to the step along with: + +1. the step class's fully qualified name, and +2. the step class's `VERSION` class variable (an arbitrary string). + +Unlike other workflow engines like [redun](https://github.com/insitro/redun), Tango does *not* take into account the source code of the class itself (other than its fully qualified name) because we've found that using a hash of the source code bytes is way too sensitive and less transparent for users. +When you change the source code of your step in a meaningful way you can just manually change the `VERSION` class variable to indicate to Tango +that the step has been updated. + +<!-- end faq --> + +## Team + +<!-- start team --> + +**ai2-tango** is developed and maintained by the AllenNLP team, backed by [the Allen Institute for Artificial Intelligence (AI2)](https://allenai.org/). +AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering. +To learn more about who specifically contributed to this codebase, see [our contributors](https://github.com/allenai/tango/graphs/contributors) page. + +<!-- end team --> + +## License + +<!-- start license --> + +**ai2-tango** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). +A full copy of the license can be found [on GitHub](https://github.com/allenai/tango/blob/main/LICENSE). + +<!-- end license --> + + +%prep +%autosetup -n ai2-tango-1.2.1 + +%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-ai2-tango -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.1-1 +- Package Spec generated @@ -0,0 +1 @@ +faa9c628e057896526f15f7f0771acb1 ai2-tango-1.2.1.tar.gz |