[](https://badge.fury.io/py/experimaestro)
[](https://experimaestro-python.readthedocs.io)
Experimaestro helps in designing and managing complex workflows. It allows for the definition of tasks and their dependencies, ensuring that each step in a workflow is executed in the correct order. Some key aspects of Experimaestro are:
- **Task Automation**: The tool automates repetitive tasks, making it easier to run large-scale experiments. It's particularly useful in scenarios where experiments need to be repeated with different parameters or datasets.
- **Resource Management**: It efficiently manages computational resources, which is critical when dealing with data-intensive tasks or when running multiple experiments in parallel.
- **Extensibility**: Experimaestro is designed to be flexible and extensible, allowing users to integrate it with various programming languages and tools commonly used in data science and research.
- **Reproducibility**: By keeping a detailed record of experiments, including parameters and environments, it aids in ensuring the reproducibility of scientific experiments, which is a fundamental requirement in research.
- **User Interface**: While primarily a back-end tool, Experimaestro also offers a user interface to help in managing and visualizing workflows.
The full documentation can be read by going to the following URL: [https://experimaestro-python.readthedocs.io](https://experimaestro-python.readthedocs.io)
# Install
## With pip
You can then install the package using `pip install experimaestro`
## Develop
Checkout the git directory, then
```
pip install -e .
```
# Example
This very simple example shows how to submit two tasks that concatenate two strings.
Under the curtain,
- A directory is created for each task (in `workdir/jobs/helloworld.add/HASHID`)
based on a unique ID computed from the parameters
- Two processes for `Say` are launched (there are no dependencies, so they will be run in parallel)
- A tag `y` is created for the main task
<!-- SNIPPET: MAIN ARGS[%WORKDIR% --port 0 --sleeptime=0.0001] -->
```python
# --- Task and types definitions
import logging
logging.basicConfig(level=logging.DEBUG)
from pathlib import Path
from experimaestro import Task, Param, experiment, progress
import click
import time
import os
from typing import List
# --- Just to be able to monitor the tasks
def slowdown(sleeptime: int, N: int):
logging.info("Sleeping %ds after each step", sleeptime)
for i in range(N):
time.sleep(sleeptime)
progress((i+1)/N)
# --- Define the tasks
class Say(Task):
word: Param[str]
sleeptime: Param[float]
def execute(self):
slowdown(self.sleeptime, len(self.word))
print(self.word.upper(),)
class Concat(Task):
strings: Param[List[Say]]
sleeptime: Param[float]
def execute(self):
says = []
slowdown(self.sleeptime, len(self.strings))
for string in self.strings:
with open(string.__xpm_stdout__) as fp:
says.append(fp.read().strip())
print(" ".join(says))
# --- Defines the experiment
@click.option("--port", type=int, default=12345, help="Port for monitoring")
@click.option("--sleeptime", type=float, default=2, help="Sleep time")
@click.argument("workdir", type=Path)
@click.command()
def cli(port, workdir, sleeptime):
"""Runs an experiment"""
# Sets the working directory and the name of the xp
with experiment(workdir, "helloworld", port=port) as xp:
# Submit the tasks
hello = Say.C(word="hello", sleeptime=sleeptime).submit()
world = Say.C(word="world", sleeptime=sleeptime).submit()
# Concat will depend on the two first tasks
Concat.C(strings=[hello, world], sleeptime=sleeptime).tag("y", 1).submit()
if __name__ == "__main__":
cli()
```
which can be launched with `python test.py /tmp/helloworld-workdir`
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"description": "[](https://badge.fury.io/py/experimaestro)\n[](https://experimaestro-python.readthedocs.io)\n\nExperimaestro helps in designing and managing complex workflows. It allows for the definition of tasks and their dependencies, ensuring that each step in a workflow is executed in the correct order. Some key aspects of Experimaestro are:\n\n- **Task Automation**: The tool automates repetitive tasks, making it easier to run large-scale experiments. It's particularly useful in scenarios where experiments need to be repeated with different parameters or datasets.\n- **Resource Management**: It efficiently manages computational resources, which is critical when dealing with data-intensive tasks or when running multiple experiments in parallel.\n- **Extensibility**: Experimaestro is designed to be flexible and extensible, allowing users to integrate it with various programming languages and tools commonly used in data science and research.\n- **Reproducibility**: By keeping a detailed record of experiments, including parameters and environments, it aids in ensuring the reproducibility of scientific experiments, which is a fundamental requirement in research.\n- **User Interface**: While primarily a back-end tool, Experimaestro also offers a user interface to help in managing and visualizing workflows.\n\nThe full documentation can be read by going to the following URL: [https://experimaestro-python.readthedocs.io](https://experimaestro-python.readthedocs.io)\n\n\n# Install\n\n## With pip\n\nYou can then install the package using `pip install experimaestro`\n\n## Develop\n\nCheckout the git directory, then\n\n```\npip install -e .\n```\n\n# Example\n\nThis very simple example shows how to submit two tasks that concatenate two strings.\nUnder the curtain,\n\n- A directory is created for each task (in `workdir/jobs/helloworld.add/HASHID`)\n based on a unique ID computed from the parameters\n- Two processes for `Say` are launched (there are no dependencies, so they will be run in parallel)\n- A tag `y` is created for the main task\n\n<!-- SNIPPET: MAIN ARGS[%WORKDIR% --port 0 --sleeptime=0.0001] -->\n\n```python\n# --- Task and types definitions\n\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nfrom pathlib import Path\nfrom experimaestro import Task, Param, experiment, progress\nimport click\nimport time\nimport os\nfrom typing import List\n\n# --- Just to be able to monitor the tasks\n\ndef slowdown(sleeptime: int, N: int):\n logging.info(\"Sleeping %ds after each step\", sleeptime)\n for i in range(N):\n time.sleep(sleeptime)\n progress((i+1)/N)\n\n\n# --- Define the tasks\n\nclass Say(Task):\n word: Param[str]\n sleeptime: Param[float]\n\n def execute(self):\n slowdown(self.sleeptime, len(self.word))\n print(self.word.upper(),)\n\nclass Concat(Task):\n strings: Param[List[Say]]\n sleeptime: Param[float]\n\n def execute(self):\n says = []\n slowdown(self.sleeptime, len(self.strings))\n for string in self.strings:\n with open(string.__xpm_stdout__) as fp:\n says.append(fp.read().strip())\n print(\" \".join(says))\n\n\n# --- Defines the experiment\n\n@click.option(\"--port\", type=int, default=12345, help=\"Port for monitoring\")\n@click.option(\"--sleeptime\", type=float, default=2, help=\"Sleep time\")\n@click.argument(\"workdir\", type=Path)\n@click.command()\ndef cli(port, workdir, sleeptime):\n \"\"\"Runs an experiment\"\"\"\n # Sets the working directory and the name of the xp\n with experiment(workdir, \"helloworld\", port=port) as xp:\n # Submit the tasks\n hello = Say.C(word=\"hello\", sleeptime=sleeptime).submit()\n world = Say.C(word=\"world\", sleeptime=sleeptime).submit()\n\n # Concat will depend on the two first tasks\n Concat.C(strings=[hello, world], sleeptime=sleeptime).tag(\"y\", 1).submit()\n\n\nif __name__ == \"__main__\":\n cli()\n```\n\nwhich can be launched with `python test.py /tmp/helloworld-workdir`\n",
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