# LXM3: XManager launch backend for HPC clusters
[](https://badge.fury.io/py/lxm3)
[](https://pdm-project.org)
[](https://github.com/astral-sh/ruff)
lxm3 provides an implementation for DeepMind's [XManager](https://github.com/deepmind/xmanager/tree/main) launch API that aims to provide a similar experience for running experiments on traditional HPC. It provides a local execution backend and support for the [SGE](https://en.wikipedia.org/wiki/Oracle_Grid_Engine) and [Slurm](https://slurm.schedmd.com/) schedulers.
<img src="docs/logo.png" alt="logo" style="width:400px;"/>
_LXM3 Image created by GPT-4_
__NOTE__: lxm3 is still in early development. The API is not stable and may change in the future. We periodically update tag versions which are considered in good shape.
If you use lxm3 in your project, please pin to a specific commit/tag to avoid
breaking changes.
## Installation
For running on a cluster, you should install Singularity and rsync before using lxm3.
It may be possible to run on the cluster without Singularity, but that path
was not tested thoroughly.
You can install lxm3 from PyPI by running.
```bash
pip install lxm3
```
You can also install from GitHub for the latest features.
```bash
# Consider pinning to a specific commit/tag.
pip install git+https://github.com/ethanluoyc/lxm3
```
## Prerequisites
### Set up configuration file (required)
You should create a configuration file for setting up credentials and storage location
for your cluster. In addition, the configuration file is also required to specify the
storage location for the local executor.
Create a configuration file at `$XDG_CONFIG_HOME/lxm3/config.toml` (defaults to `~/.config/lxm3/config.toml`) with the following content:
```toml
project = "" # Optional project name
# Configuration for running in local mode.
[local]
[local.storage]
# Configuration to lxm3 to stage local artifacts.
staging = "~/.cache/lxm3"
# Configuration for running on clusters. Omit if you are only using local mode.
[[clusters]]
# Set a name for this cluster, e.g., "cs"
name = "<TODO>"
# Replace with the server you normally use for ssh into the cluster, e.g. "beaker.cs.ucl.ac.uk"
server = "<TODO>"
# Fill in the username you use for this cluster.
user = "<TODO>"
# Uncomment and update the line below if you would like to use a private key file ssh.
# ssh_private_key = "~/.ssh/<private key name>"
# Uncomment and update the line below if you would like to use a password for ssh.
# password = "<password>"
# Uncomment and update the line below if you need to connect to the cluster
# via a jump server. This corresponds to the proxycommand option in ssh_config.
# proxycommand = ""
[clusters.storage]
# Replace with the path to a staging directory on the cluster. lxm3 uses this directory for storing all files required to run your job.
# This should be an absolute directory and should not be a symlink
staging = "<absolute path to your home directory>/lxm3-staging"
```
### Install Singularity/Apptainer (optional)
If you use the `SingularityContainer` executable, you should install Singularity/Apptainer
on your machine. Your cluster should have installed Singularity/Apptainer on your HPC cluster as well.
Follow the instructions on the [singularity website](https://docs.sylabs.io/guides/4.1/admin-guide/installation.html) or [apptainer website](https://apptainer.org/docs/admin/latest/installation.html) to install Singularity/Apptainer respectively. Currently, lxm3
supports Apptainer via the singularity symlink.
### Install Docker (optional)
We recommend installing Docker as well even though you are using Singularity/Apptainer.
This would allow your to use Docker's build cache to speed up the build process. An experimental
`DockerContainer` is also provided for running jobs with Docker.
## Writing lxm3 launch scripts
At a high level you can launch experiment by creating a launch script
called `launcher.py` that looks like:
```python
# Create an experiment and acquite its context
with xm_cluster.create_experiment(experiment_title="hello world") as experiment:
# Define an specification for the executable you want to run
spec = xm_cluster.PythonPackage(
path=".",
entrypoint=xm_cluster.ModuleName("my_package.main"),
)
# Define an executor for the executable
# To launch locally
executor = xm_cluster.Local()
# or, if you want to use SGE:
# executor = xm_cluster.GridEngine()
# or, if you want to run on a Slurm cluster:
executor = xm_cluster.Slurm()
# package your code
[executable] = experiment.package(
[xm.Packageable(spec, executor_spec=executor.Spec())]
)
# add jobs to your experiment
experiment.add(
xm.Job(executable=executable, executor=executor)
)
```
and launch the experimet from the command line with
```python
lxm3 launch launcher.py
```
Many things happen under the hood. Since lxm3 implements the XManager
API, you should get familiar with the concepts in the
[XManager](https://github.com/deepmind/xmanager). Once you are
familiar with the concepts, checkout the [examples/](examples/)
directory for a quick start guide.
## Components
lxm3 provides the following executable specification and executors.
### Executable specifications
| Name | Description |
| ----------- | ----------- |
| `lxm3.xm_cluster.PythonPackage` | A python application packageable with pip |
| `lxm3.xm_cluster.UniversalPackage` | A universal package |
| `lxm3.xm_cluster.SingularityContainer` | An executable running with Singularity |
### Executors
| Name | Description |
| ----------- | ----------- |
| `lxm3.xm_cluster.Local` | Runs a executable locally, mainly used for testing |
| `lxm3.xm_cluster.GridEngine` | Runs a executable on SGE cluster |
| `lxm3.xm_cluster.Slurm` | Runs a executable on Slurm cluster |
### Jobs
* Currently, only `xm.Job` and `xm.JobGenerator` that generates `xm.Job` are supported.
* We support HPC array jobs via `xm_cluster.ArrayJob`. See below.
## Implementation Details
### __Managing Dependencies with Containers__
lxm3 uses of Singularity containers for running jobs on HPCs.
lxm3 aims at providing a easy workflow for launching jobs on traditional HPC clusters,
which deviates from typical workflows for launching experiments on Cloud platforms.
lxm3 is designed for working with containerized applications using [Singularity](https://docs.sylabs.io/guides/3.5/user-guide/introduction.html) as the runtime.
Singularity is a popular choice for HPC clusters because it allows users to run containers without requiring root privileges, and is supported by many HPC clusters worldwide.
There are many benefits to using containers for running jobs on HPCs compared to traditional isolation via `venv` or `conda`.
1. `venv` and `conda` are laid out as a directory of files on the environment. For many
HPCs, normally these will be installed on a networked filesystem such as NFS. Operations
on these virtual environments are slow and inefficient. For example, on our cluster, removing
a `conda` environment with many dependencies can take an hour when these environments are
on NFS. There are usually quota put in places not only for the file sizes but also the number of files.
For ML projects that uses depends on many (large) packages such as TensorFlow, PyTorch, it is very easy
to hit the quota limit. Singularity containers are a single file. This is both easy
for deployment and also avoids the file number quota.
2. Containers provide consistent environment for running jobs on different clusters as well as making it easy to use system dependencies not installed on HPC's host environment.
### __Automated Deployment__.
HPC deployments normally use a filesystem that are detached from the filesystems of the user's workstation.
Many tutorials for running jobs on HPCs request the users to either clone their repository on the login node or ask the users manually copy files to the cluster. Doing this repeatedly is tedious. lxm3 automates the deployments from your workstation to the HPC cluster so that you can do most of your work locally without having
directly login into the cluster.
Unlike Docker or other OCI images that are composed of multiple layers,
the Singularity Image Format (SIF) used by Singularity is a single file that contains the entire filesystem of the container. While this is convenient as deployment to a remote cluster can be performed with a single `scp/rsync` command. The lack of layer caching/sharing makes repeated deployments slow and inefficient.
For this reason, unlike typical cloud deployments where the application and dependencies are packaged into a single image, lxm3 uses a two-stage packaging process to separate the application and dependencies.
This allows applications with heavy dependencies to be packaged once and reused across multiple experiments by reusing the same singularity container.
For Python applications, we rely on the user to first build a runtime image for all of the dependencies and use [standard Python pacakging](https://packaging.python.org/en/latest/tutorials/packaging-projects/) tools to create a distribution that is deployed separately to the cluster.
Concretely, the user is expected to create a simple `pyproject.toml` which describes how to create a distribution for their applications.
This is convenient, as lxm3 does not have to invent a custom packaging format for Python applications. For example, a simple `pyproject.toml` that uses `hatchling` as the build backend looks like:
```toml
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project]
name = "py_package"
authors = [{ name = "Joe" }]
version = "0.1.0"
```
lxm3 uses `pip install --no-deps` to create a zip archive that contains all of your application code.
In addition to the packaging, lxm3 also allows you to carry extra files for your deployment via
`xm_cluster.Fileset`. This is for example useful if you want to deploy configuration files.
The zip archive is automatically extracted into a temporary directory on the cluster and executed from there.
Using a zip archive again minimizes the number of files that are deployed to the cluster so that you are
less likely to hit a file number limit.
If you are using a different language or you cannot package your python application easily
with standard Python packaging tools, you can use `xm_cluster.UniversalPackage` to package your application.
### __Easy Hyperparameter Sweeping__.
For many scientific research projects, it's common to run the same experiment with different hyperparameters. lxm3 automatically generates jobs scripts that can be submitted to the cluster's scheduler for running multiple experiments with different hyperparameters passed as differnt command line arguments or environment variables.
For large parameter sweep, launching many separate jobs at once can
overwhelm the scheduler. For this reason, HPC schedulers encourage the
use of job arrays to submit sweeps. lxm3 provide a `ArrayJob`
xm.JobConfig that allows you to submit multiple jobs with the same
executable and job requirements but different hyperparameters as a
single job array.
For example:
```python
from lxm3 import xm
from lxm3 import xm_cluster
with xm_cluster.create_experiment() as experiment:
executable = ...
executor = ...
parameters = [{"seed": seed} for seed in range(5)]
experiment.add(
xm_cluster.ArrayJob(executable=executable, executor=executor, args=parameters)
)
```
This will be translated as passing `--seed {0..4}` to your executable. We
also support customzing environment variables, which is convenient for example if you
use [Weights and Biases](https://wandb.ai/site) where you can configure run names and groups
from environment variables (TODO(yl): migrate examples for configuring wandb).
There is a lot of flexibility on how to create the `args` for each job.
For example, you can use `itertools.product` to create a cartesian product of all the hyperparameters.
You can create arbitrary sweeps in pure python without resorting to a DSL.
Under the hood, lxm3 automatically generates job scripts that map from the array job index
to command line arguments and environment variables.
### __Separate experiment launching from your application__.
Similar to the design of XManager, lxm3 separates the launching of experiments from your application
so that you are not bound to a specific experiment framework.
You can develop your project with your favorite framework without having your application
code be aware of the existence of lxm3. In fact, we recommend that you install lxm3
as a development dependency that are not bundled with your dependencies used at runtime.
You can also install lxm3 globally or in its own virtual environment via `pex` or `pipx`.
### Notes for existing Xmanager users
1. We vendored a copy of xmanager core API (v0.4.0) into lxm3 with
light modification to support Python 3.9. This also allows us to just use the launch API
without `xm_local`'s dependencies. Thus, you should import the core API as
`from lxm3 import xm` instead of `from xmanager import xm`. Our executables specs
are defined in `xm_cluster` instead `xm` as we do not support the executable specs
from the core API.
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"description": "# LXM3: XManager launch backend for HPC clusters\n[](https://badge.fury.io/py/lxm3)\n\n\n[](https://pdm-project.org)\n[](https://github.com/astral-sh/ruff)\n\nlxm3 provides an implementation for DeepMind's [XManager](https://github.com/deepmind/xmanager/tree/main) launch API that aims to provide a similar experience for running experiments on traditional HPC. It provides a local execution backend and support for the [SGE](https://en.wikipedia.org/wiki/Oracle_Grid_Engine) and [Slurm](https://slurm.schedmd.com/) schedulers.\n\n<img src=\"docs/logo.png\" alt=\"logo\" style=\"width:400px;\"/>\n\n_LXM3 Image created by GPT-4_\n\n__NOTE__: lxm3 is still in early development. The API is not stable and may change in the future. We periodically update tag versions which are considered in good shape.\nIf you use lxm3 in your project, please pin to a specific commit/tag to avoid\nbreaking changes.\n\n## Installation\nFor running on a cluster, you should install Singularity and rsync before using lxm3.\nIt may be possible to run on the cluster without Singularity, but that path\nwas not tested thoroughly.\n\nYou can install lxm3 from PyPI by running.\n```bash\npip install lxm3\n```\nYou can also install from GitHub for the latest features.\n```bash\n# Consider pinning to a specific commit/tag.\npip install git+https://github.com/ethanluoyc/lxm3\n```\n\n## Prerequisites\n\n### Set up configuration file (required)\nYou should create a configuration file for setting up credentials and storage location\nfor your cluster. In addition, the configuration file is also required to specify the\nstorage location for the local executor.\n\nCreate a configuration file at `$XDG_CONFIG_HOME/lxm3/config.toml` (defaults to `~/.config/lxm3/config.toml`) with the following content:\n\n```toml\nproject = \"\" # Optional project name\n# Configuration for running in local mode.\n[local]\n[local.storage]\n# Configuration to lxm3 to stage local artifacts.\nstaging = \"~/.cache/lxm3\"\n\n# Configuration for running on clusters. Omit if you are only using local mode.\n[[clusters]]\n# Set a name for this cluster, e.g., \"cs\"\nname = \"<TODO>\"\n# Replace with the server you normally use for ssh into the cluster, e.g. \"beaker.cs.ucl.ac.uk\"\nserver = \"<TODO>\"\n# Fill in the username you use for this cluster.\nuser = \"<TODO>\"\n# Uncomment and update the line below if you would like to use a private key file ssh.\n# ssh_private_key = \"~/.ssh/<private key name>\"\n# Uncomment and update the line below if you would like to use a password for ssh.\n# password = \"<password>\"\n# Uncomment and update the line below if you need to connect to the cluster\n# via a jump server. This corresponds to the proxycommand option in ssh_config.\n# proxycommand = \"\"\n\n[clusters.storage]\n# Replace with the path to a staging directory on the cluster. lxm3 uses this directory for storing all files required to run your job.\n# This should be an absolute directory and should not be a symlink\nstaging = \"<absolute path to your home directory>/lxm3-staging\"\n\n```\n\n### Install Singularity/Apptainer (optional)\nIf you use the `SingularityContainer` executable, you should install Singularity/Apptainer\non your machine. Your cluster should have installed Singularity/Apptainer on your HPC cluster as well.\nFollow the instructions on the [singularity website](https://docs.sylabs.io/guides/4.1/admin-guide/installation.html) or [apptainer website](https://apptainer.org/docs/admin/latest/installation.html) to install Singularity/Apptainer respectively. Currently, lxm3\nsupports Apptainer via the singularity symlink.\n\n### Install Docker (optional)\nWe recommend installing Docker as well even though you are using Singularity/Apptainer.\nThis would allow your to use Docker's build cache to speed up the build process. An experimental\n`DockerContainer` is also provided for running jobs with Docker.\n\n## Writing lxm3 launch scripts\nAt a high level you can launch experiment by creating a launch script\ncalled `launcher.py` that looks like:\n\n```python\n# Create an experiment and acquite its context\nwith xm_cluster.create_experiment(experiment_title=\"hello world\") as experiment:\n\n # Define an specification for the executable you want to run\n spec = xm_cluster.PythonPackage(\n path=\".\",\n entrypoint=xm_cluster.ModuleName(\"my_package.main\"),\n )\n\n # Define an executor for the executable\n # To launch locally\n executor = xm_cluster.Local()\n # or, if you want to use SGE:\n # executor = xm_cluster.GridEngine()\n # or, if you want to run on a Slurm cluster:\n executor = xm_cluster.Slurm()\n\n # package your code\n [executable] = experiment.package(\n [xm.Packageable(spec, executor_spec=executor.Spec())]\n )\n\n # add jobs to your experiment\n experiment.add(\n xm.Job(executable=executable, executor=executor)\n )\n```\nand launch the experimet from the command line with\n```python\nlxm3 launch launcher.py\n```\n\nMany things happen under the hood. Since lxm3 implements the XManager\nAPI, you should get familiar with the concepts in the\n[XManager](https://github.com/deepmind/xmanager). Once you are\nfamiliar with the concepts, checkout the [examples/](examples/)\ndirectory for a quick start guide.\n\n\n## Components\nlxm3 provides the following executable specification and executors.\n\n### Executable specifications\n| Name | Description |\n| ----------- | ----------- |\n| `lxm3.xm_cluster.PythonPackage` | A python application packageable with pip |\n| `lxm3.xm_cluster.UniversalPackage` | A universal package |\n| `lxm3.xm_cluster.SingularityContainer` | An executable running with Singularity |\n\n### Executors\n| Name | Description |\n| ----------- | ----------- |\n| `lxm3.xm_cluster.Local` | Runs a executable locally, mainly used for testing |\n| `lxm3.xm_cluster.GridEngine` | Runs a executable on SGE cluster |\n| `lxm3.xm_cluster.Slurm` | Runs a executable on Slurm cluster |\n\n### Jobs\n* Currently, only `xm.Job` and `xm.JobGenerator` that generates `xm.Job` are supported.\n* We support HPC array jobs via `xm_cluster.ArrayJob`. See below.\n\n## Implementation Details\n### __Managing Dependencies with Containers__\nlxm3 uses of Singularity containers for running jobs on HPCs.\n\nlxm3 aims at providing a easy workflow for launching jobs on traditional HPC clusters,\nwhich deviates from typical workflows for launching experiments on Cloud platforms.\n\nlxm3 is designed for working with containerized applications using [Singularity](https://docs.sylabs.io/guides/3.5/user-guide/introduction.html) as the runtime.\nSingularity is a popular choice for HPC clusters because it allows users to run containers without requiring root privileges, and is supported by many HPC clusters worldwide.\n\nThere are many benefits to using containers for running jobs on HPCs compared to traditional isolation via `venv` or `conda`.\n\n1. `venv` and `conda` are laid out as a directory of files on the environment. For many\nHPCs, normally these will be installed on a networked filesystem such as NFS. Operations\non these virtual environments are slow and inefficient. For example, on our cluster, removing\na `conda` environment with many dependencies can take an hour when these environments are\non NFS. There are usually quota put in places not only for the file sizes but also the number of files.\nFor ML projects that uses depends on many (large) packages such as TensorFlow, PyTorch, it is very easy\nto hit the quota limit. Singularity containers are a single file. This is both easy\nfor deployment and also avoids the file number quota.\n2. Containers provide consistent environment for running jobs on different clusters as well as making it easy to use system dependencies not installed on HPC's host environment.\n\n### __Automated Deployment__.\nHPC deployments normally use a filesystem that are detached from the filesystems of the user's workstation.\nMany tutorials for running jobs on HPCs request the users to either clone their repository on the login node or ask the users manually copy files to the cluster. Doing this repeatedly is tedious. lxm3 automates the deployments from your workstation to the HPC cluster so that you can do most of your work locally without having\ndirectly login into the cluster.\n\nUnlike Docker or other OCI images that are composed of multiple layers,\nthe Singularity Image Format (SIF) used by Singularity is a single file that contains the entire filesystem of the container. While this is convenient as deployment to a remote cluster can be performed with a single `scp/rsync` command. The lack of layer caching/sharing makes repeated deployments slow and inefficient.\nFor this reason, unlike typical cloud deployments where the application and dependencies are packaged into a single image, lxm3 uses a two-stage packaging process to separate the application and dependencies.\nThis allows applications with heavy dependencies to be packaged once and reused across multiple experiments by reusing the same singularity container.\n\nFor Python applications, we rely on the user to first build a runtime image for all of the dependencies and use [standard Python pacakging](https://packaging.python.org/en/latest/tutorials/packaging-projects/) tools to create a distribution that is deployed separately to the cluster.\nConcretely, the user is expected to create a simple `pyproject.toml` which describes how to create a distribution for their applications.\nThis is convenient, as lxm3 does not have to invent a custom packaging format for Python applications. For example, a simple `pyproject.toml` that uses `hatchling` as the build backend looks like:\n```toml\n[build-system]\nrequires = [\"hatchling\"]\nbuild-backend = \"hatchling.build\"\n\n[project]\nname = \"py_package\"\nauthors = [{ name = \"Joe\" }]\nversion = \"0.1.0\"\n```\nlxm3 uses `pip install --no-deps` to create a zip archive that contains all of your application code.\nIn addition to the packaging, lxm3 also allows you to carry extra files for your deployment via\n`xm_cluster.Fileset`. This is for example useful if you want to deploy configuration files.\n\nThe zip archive is automatically extracted into a temporary directory on the cluster and executed from there.\nUsing a zip archive again minimizes the number of files that are deployed to the cluster so that you are\nless likely to hit a file number limit.\n\nIf you are using a different language or you cannot package your python application easily\nwith standard Python packaging tools, you can use `xm_cluster.UniversalPackage` to package your application.\n\n### __Easy Hyperparameter Sweeping__.\nFor many scientific research projects, it's common to run the same experiment with different hyperparameters. lxm3 automatically generates jobs scripts that can be submitted to the cluster's scheduler for running multiple experiments with different hyperparameters passed as differnt command line arguments or environment variables.\n\nFor large parameter sweep, launching many separate jobs at once can\noverwhelm the scheduler. For this reason, HPC schedulers encourage the\nuse of job arrays to submit sweeps. lxm3 provide a `ArrayJob`\nxm.JobConfig that allows you to submit multiple jobs with the same\nexecutable and job requirements but different hyperparameters as a\nsingle job array.\n\nFor example:\n```python\nfrom lxm3 import xm\nfrom lxm3 import xm_cluster\nwith xm_cluster.create_experiment() as experiment:\n executable = ...\n executor = ...\n parameters = [{\"seed\": seed} for seed in range(5)]\n experiment.add(\n xm_cluster.ArrayJob(executable=executable, executor=executor, args=parameters)\n )\n```\nThis will be translated as passing `--seed {0..4}` to your executable. We\nalso support customzing environment variables, which is convenient for example if you\nuse [Weights and Biases](https://wandb.ai/site) where you can configure run names and groups\nfrom environment variables (TODO(yl): migrate examples for configuring wandb).\n\nThere is a lot of flexibility on how to create the `args` for each job.\nFor example, you can use `itertools.product` to create a cartesian product of all the hyperparameters.\nYou can create arbitrary sweeps in pure python without resorting to a DSL.\n\nUnder the hood, lxm3 automatically generates job scripts that map from the array job index\nto command line arguments and environment variables.\n\n### __Separate experiment launching from your application__.\nSimilar to the design of XManager, lxm3 separates the launching of experiments from your application\nso that you are not bound to a specific experiment framework.\nYou can develop your project with your favorite framework without having your application\ncode be aware of the existence of lxm3. In fact, we recommend that you install lxm3\nas a development dependency that are not bundled with your dependencies used at runtime.\nYou can also install lxm3 globally or in its own virtual environment via `pex` or `pipx`.\n\n### Notes for existing Xmanager users\n\n1. We vendored a copy of xmanager core API (v0.4.0) into lxm3 with\nlight modification to support Python 3.9. This also allows us to just use the launch API\nwithout `xm_local`'s dependencies. Thus, you should import the core API as\n`from lxm3 import xm` instead of `from xmanager import xm`. Our executables specs\nare defined in `xm_cluster` instead `xm` as we do not support the executable specs\nfrom the core API.\n",
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