GitHub - scottclowe/pytorch-experiment-template

PyTorch Experiment Template

This repository gives a fully-featured template or skeleton for new PyTorch experiments for use on the Vector Institute cluster. It supports:

multi-node, multi-GPU jobs using DistributedDataParallel (DDP),
preemption handling which gracefully stops and resumes the job,
logging experiments to Weights & Biases,
configuration seemlessly scales up as the amount of resources increases.

If you want to run the example experiment, you can clone the repository as-is without modifying it. Then see the Installation and Executing the example experiment sections below.

If you want to create a new repository from this template, you should follow the Creating a git repository using this template instructions first.

Creating a git repository using this template

When creating a new repository from this template, these are the steps to follow:

Don't click the fork button. The fork button is for making a new template based in this one, not for using the template to make a new repository.
Create repository.
1. New GitHub repository.
  
  You can create a new repository on GitHub from this template by clicking the Use this template button.
  
  Then clone your new repository to your local system [pseudocode]:
```
git clone [email protected]:your_org/your_repo_name.git
cd your_repo_name
```
2. New repository not on GitHub.
  
  Alternatively, if your new repository is not going to be on GitHub, you can download this repo as a zip and work from there.
  
  Note that this zip does not include the .gitignore and .gitattributes files (because GitHub automatically omits them, which is usually helpful but is not for our purposes). Thus you will also need to download the .gitignore and .gitattributes files.
Delete the LICENSE file and replace it with a LICENSE file of your own choosing. If the code is intended to be freely available for anyone to use, use an open source license, such as MIT License or GPLv3. If you don't want your code to be used by anyone else, add a LICENSE file which just says:
```
Copyright (c) CURRENT_YEAR, YOUR_NAME

All rights reserved.
```
Note that if you don't include a LICENSE file, you will still have copyright over your own code (this copyright is automatically granted), and your code will be private source (technically nobody else will be permitted to use it, even if you make your code publicly available).
Edit the file template_experiment/__meta__.py to contain your author and repo details.

name

The name as it would be on PyPI (users will do pip install new_name_here). It is recommended to use a name all lowercase, runtogetherwords but if separators are needed hyphens are preferred over underscores.

path

The path to the package. What you will rename the directory template_experiment. Should be the same as name, but now hyphens are disallowed and should be swapped for underscores. By default, this is automatically inferred from name.

license

Should be the name of the license you just picked and put in the LICENSE file (e.g. MIT or GPLv3).

Other fields to enter should be self-explanatory.

Rename the directory template_experiment to be the path variable you just added to __meta__.py:

# Define PROJ_HYPH as your actual project name (use hyphens instead of underscores or spaces)
PROJ_HYPH=your-actual-project-name-with-hyphens-for-spaces

# Automatically convert hyphens to underscores to get the directory name
PROJ_DIRN="${PROJ_HYPH//-/_}"
# Rename the directory
mv template_experiment "$PROJ_DIRN"

Change references to template_experiment and template-experiment to your path variable.

This can be done with the sed command:

sed -i "s/template_experiment/$PROJ_DIRN/" "$PROJ_DIRN"/*.py setup.py pyproject.toml slurm/*.slrm .pre-commit-config.yaml
sed -i "s/template-experiment/$PROJ_HYPH/" "$PROJ_DIRN"/*.py setup.py pyproject.toml slurm/*.slrm .pre-commit-config.yaml

Which will make changes in the following places.

In setup.py, L51:

exec(read("template_experiment/__meta__.py"), meta)

In __meta__.py, L2,4:
```
name = "template-experiment"
```

In train.py, L18-19:

from template_experiment import data_transformations, datasets, encoders, utils
from template_experiment.evaluation import evaluate

In train.py, L1260:

group.add_argument(
    "--wandb-project",
    type=str,
    default="template-experiment",
    help="Name of project on wandb, where these runs will be saved.",
)

In slurm/train.slrm, L19:

#SBATCH --job-name=template-experiment    # Set this to be a shorthand for your project's name.

In slurm/train.slrm, L23:
```
PROJECT_NAME="template-experiment"
```
In slurm/notebook.slrm, L15:
```
PROJECT_NAME="template-experiment"
```

Swap out the contents of README.rst with an initial description of your project. If you prefer, you can use markdown (README.md) instead of rST:
```
git rm README.rst
# touch README.rst
touch README.md && sed -i "s/.rst/.md/" MANIFEST.in
```
Add your changes to the repo's initial commit and force-push your changes:
```
git add .
git commit --amend -m "Initial commit"
git push --force
```

Installation

I recommend using miniconda to create an environment for your project. By using one virtual environment dedicated to each project, you are ensured stability - if you upgrade a package for one project, it won't affect the environments you already have established for the others.

Vector one-time set-up

Run this code block to install miniconda before you make your first environment (you don't need to re-run this every time you start a new project):

# Login to Vector
ssh [email protected]
# Enter your password and 2FA code to login.
# Run the rest of this code block on the gateway node of the cluster that
# you get to after establishing the ssh connection.

# Make a screen session for us to work in
screen;

# Download miniconda to your ~/Downloads directory
mkdir -p $HOME/Downloads;
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
    -O "$HOME/Downloads/miniconda.sh";
# Install miniconda to the home directory, if it isn't there already.
if [ ! -d "$HOME/miniconda/bin" ]; then
    if [ -d "$HOME/miniconda" ]; then rm -r "$HOME/miniconda"; fi;
    bash $HOME/Downloads/miniconda.sh -b -p "$HOME/miniconda";
fi;

# Add conda to the PATH environment variable
export PATH="$HOME/miniconda/bin:$PATH";

# Automatically say yes to any check from conda (optional)
conda config --set always_yes yes

# Set the command prompt prefix to be the name of the current venv
conda config --set env_prompt '({name}) '

# Add conda setup to your ~/.bashrc file
conda init;

# Now exit this screen session (you have to exit the current terminal
# session after conda init, and exiting the screen session achieves that
# without closing the ssh connection)
exit;

Follow this next step if you want to use Weights and Biases to log your experiments. Weights and Biases is an online service for tracking your experiments which is free for academic usage. To set this up, you need to install the wandb pip package, and you'll need to create a Weights and Biases account if you don't already have one:

# (On v.vectorinstitute.ai)
# You need to run the conda setup instructions that miniconda added to
# your ~/.bashrc file so that conda is on your PATH and you can run it.
# Either create a new screen session - when you launch a new screen session,
# bash automatically runs source ~/.bashrc
screen;
# Or stay in your current window and explicitly yourself run
source ~/.bashrc
# Either way, you'll now see "(miniconda)" at the left of your command prompt,
# indicating miniconda is on your PATH and using your default conda environment.

# Install wandb
pip install wandb

# Log in to wandb at the command prompt
wandb login
# wandb asks you for your username, then password
# Then wandb creates a file in ~/.netrc which it uses to automatically login in the future

Project one-time set-up

Run this code block once every time you start a new project from this template. Change ENVNAME to equal the name of your project. This code will then create a new virtual environment to use for the project:

# (On v.vectorinstitute.ai)
# You need to run the conda setup instructions that miniconda added to
# your ~/.bashrc file so that conda is on your PATH and you can run it.
# Either create a new screen session - when you launch a new screen session,
# bash automatically runs source ~/.bashrc
screen;
# Or stay in your current window and explicitly yourself run
source ~/.bashrc
# Either way, you'll now see "(miniconda)" at the left of your command prompt,
# indicating miniconda is on your PATH and using your default conda environment.

# Now run the following one-time setup per virtual environment (i.e. once per project)

# Pick a name for the new environment.
# It should correspond to the name of your project (hyphen separated, no spaces)
ENVNAME=template-experiment

# Create a python3.x conda environment, with pip installed, with this name.
conda create -y --name "$ENVNAME" -q python=3 pip

# Activate the environment
conda activate "$ENVNAME"
# The command prompt should now have your environment at the left of it, e.g.
# (template-experiment) slowe@v3:~$

Resuming work on an existing project

Run this code block when you want to resume work on an existing project:

# (On v.vectorinstitute.ai)
# Run conda setup in ~/.bashrc if you it hasn't already been run in this
# terminal session
source ~/.bashrc
# The command prompt should now say (miniconda) at the left of it.

# Activate the environment
conda activate template-experiment
# The command prompt should now have your environment at the left of it, e.g.
# (template-experiment) slowe@v3:~$

Executing the example experiment

The following commands describe how to setup and run the example repository in its unmodified state.

To run the code in a repository you have created from this template, replace template-experiment with the name of your package and template_experiment with the name of your package directory, etc.

Set-up

If you haven't already, then follow the Vector one-time set-up instructions.

Then clone the repository:

git clone [email protected]:scottclowe/pytorch-experiment-template.git
cd pytorch-experiment-template

Run the Project one-time set-up (using template-experiment as the environment name).
With the project's conda environment activated, install the package and its training dependencies:
```
pip install --editable .[train]
```
This step will typically take 5-10 minutes to run.
Check the installation by running the help command:
```
python template_experiment/train.py -h
```
This should print the help message for the training script.

Example commands

To run the default training command locally:

python template_experiment/train.py

or alternatively:

template-experiment-train

Run the default training command with on the cluster with SLURM. First, ssh into the cluster and cd to the project repository. You don't need to activate the project's conda environment. Then use sbatch to add your SLURM job to the queue:
```
sbatch slurm/train.slrm
```
You can supply arguments to sbatch by including them before the path to the SLURM script. Arguments set on the command prompt like this will override the arguments in slurm/train.slrm. This is useful for customizing the job name, for example:
```
sbatch --job-name=exp_cf10_rn18 slurm/train.slrm
```
I recommend you should pretty much always customize the name of your job. The custom job name will be visible in the output of squeue -u "$USER" when browsing your active jobs (helpful if you have multiple jobs running and need to check on their status or cancel one of them). When using this codebase, the custom job name is also used in the path to the checkpoint, the path to the SLURM log file, and the name of the job on wandb.

Any arguments you include after slurm/train.slrm will be passed through to train.py.

For example, you can specify to use a pretrained model:

sbatch --job-name=exp_cf10_rn18-pt slurm/train.slrm --dataset=cifar10 --pretrained

change the architecture and dataset:

sbatch --job-name=exp_cf100_vit-pt \
    slurm/train.slrm --dataset=cifar100 --model=vit_small_patch16_224 --pretrained

or change the learning rate of the encoder:

sbatch --job-name=exp_cf10_rn18-pt_enc-lr-0.01 \
    slurm/train.slrm --dataset=cifar10 --pretrained --lr-encoder-mult=0.01

You can trivially scale up the job to run across multiple GPUs, either by changing the gres argument to use more of the GPUs on the node (up to 8 GPUs per node on the t4v2 partition, 4 GPUs per node otherwise):
```
sbatch --job-name=exp_cf10_rn18-pt_4gpu --gres=gpu:4 slurm/train.slrm --pretrained
```
or increasing the number of nodes being requested:
```
sbatch --job-name=exp_cf10_rn18-pt_2x1gpu --nodes=2 slurm/train.slrm --pretrained
```
or both:
```
sbatch --job-name=exp_cf10_rn18-pt_2x4gpu --nodes=2 --gres=gpu:4 slurm/train.slrm --pretrained
```
In each case, the amount of memory and CPUs requested in the SLURM job will automatically be scaled up with the number of GPUs requested. The total batch size will be scaled up by the number of GPUs requested too.

As you run these commands, you can see the results logged on wandb at https://wandb.ai/your-username/template-experiment

Jupyter notebook

You can use the script slurm/notebook.slrm to launch a Jupyter notebook server on one of the interactive compute nodes. This uses the methodology of https://support.vectorinstitute.ai/jupyter_notebook

You'll need to install jupyter into your conda environment to launch the notebook. After activating the environment for this project, run:

pip install -r requirements-notebook.txt

To launch a notebook server and connect to it on your local machine, perform the following steps.

Run the notebook SLURM script to launch the jupyter notebook:
```
sbatch slurm/notebook.slrm
```
The job will launch on one of the interactive nodes, and will acquire a random port on that node to serve the notebook on.

Wait for the job to start running. You can monitor it with:

squeue --me

Note the job id of the notebook job. e.g.:

(template-experiment) slowe@v2:~/pytorch-experiment-template$ squeue --me
     JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
  10618891 interacti      jnb    slowe  R       1:07      1 gpu026

Here we can see our JOBID is 10618891, and it is running on node gpu026.

Inspect the output of the job with:

cat jnb_JOBID.out

e.g.:

cat jnb_10618891.out

The output will contain the port number that the notebook server is using, and the token as follows:

To access the server, open this file in a browser:
    file:///ssd005/home/slowe/.local/share/jupyter/runtime/jpserver-7885-open.html
Or copy and paste one of these URLs:
    http://gpu026:47201/tree?token=f54c10f52e3dad08e19101149a54985d1561dca7eec96b29
    http://127.0.0.1:47201/tree?token=f54c10f52e3dad08e19101149a54985d1561dca7eec96b29

Here we can see the job is on node gpu026 and the notebook is being served on port 47201. We will need to use the token f54c10f52e3dad08e19101149a54985d1561dca7eec96b29 to log in to the notebook.

On your local machine, use ssh to forward the port from the compute node to your local machine:
```
ssh [email protected] -N -L 8887:gpu026:47201
```
You need to replace USERNAME with your Vector username, gpu026 with the node your job is running on, and 47201 with the port number from the previous step. In this example, the local port which the notebook is being forwarded to is port 8887.
Open a browser on your local machine and navigate to http://localhost:8887 (or whatever port you chose in the previous step):
```
sensible-browser http://localhost:8887
```
You should see the Jupyter notebook interface. Copy the token from the URL shown in the log file and paste it into the Password or token: [ ] Log in box. You should now have access to the remote notebook server on your local machine.
Once you are done working in your notebooks (and have saved your changes), make sure to end the job running the notebook with:
```
scancel JOBID
```
e.g.:
```
scancel 10618891
```
This will free up the interactive GPU node for other users to use.

Note that you can skip the need to copy the access token if you set up Jupyter notebook to use a password instead.

Features

This template includes the following features.

Scalable training script

The SLURM training script slurm/train.slrm will interface with the python training script template_experiment/train.py to train a model on multiple GPUs across, multiple nodes, using DistributedDataParallel (DDP).

The SLURM script is configured to scale up the amount of RAM and CPUs requested with the GPUs requested.

The arguments to the python script control the batch size per GPU, and the learning rate for a fixed batch size of 128 samples. The total batch size will automatically scale up when deployed on more GPUs, and the learning rate will automatically scale up linearly with the total batch size. (This is the linear scaling rule from Training ImageNet in 1 Hour.)

Preemptable

Everything is set up to resume correctly if the job is interrupted by preemption.

Checkpoints

The training script will save a checkpoint every epoch, and will resume from this if the job is interrupted by preemption.

The checkpoint for a job will be saved to the directory /checkpoint/USERNAME/PROJECT__JOBNAME__JOBID (with double-underscores between each category) along with a record of the conda environment and frozen pip requirements used to run the job in environment.yml and frozen-requirements.txt.

Log messages

Any print statements and error messages from the training script will be saved to the file slogs/JOBNAME__JOBID_ARRAYID.out. Only the output from the rank 0 worker (the worker which saves the checkpoints and sends logs to wandb) will be saved to this file. When using multiple nodes, the output from each node will be saved to a separate file: slogs-inner/JOBNAME__JOBID_ARRAYID-NODERANK.out.

You can monitor the progress of a job that is currently running by monitoring the contents of its log file. For example:

tail -n 50 -f slogs/JOBNAME__JOBID_ARRAYID.out

Weights and Biases

Weights and Biases (wandb) is an online service for tracking your experiments which is free for academic usage.

This template repository is set up to automatically log your experiments, using the same job label across both SLURM and wandb.

If the job is preempted, the wandb logging will resume to the same wandb job ID instead of spawning a new one.

Random Number Generator (RNG) state

All RNG states are configured based on the overall seed that is set with the --seed argument to train.py.

When running train.py directly, the seed is not set by default, so behaviour will not be reproducible. You will need to include the argument --seed=0 (for example), to make sure your experiments are reproducible.

When running on SLURM with slurm/train.slrm, the seed is set by default. The seed used is equal the array ID of the job. This configuration lets you easily run the same job with multiple seeds in one sbatch command. Our default job array in slurm/train.slrm is --array=0, so only one job will be launched, and that job will use the default seed of 0.

To launch the same job 5 times, each with a different seed (1, 2, 3, 4, and 5):

sbatch --array=1-5 slurm/train.slrm

or to use seeds 42 and 888:

sbatch --array=42,888 slurm/train.slrm

or to use a randomly selected seed:

sbatch --array="$RANDOM" slurm/train.slrm

The seed is used to set the following RNG states:

Each epoch gets its own RNG seed (derived from the overall seed and the epoch number). The RNG state is set with this seed at the start of each epoch. This makes it possible to resume from preemption without needing to save all the RNG states to the model checkpoint and restore them on resume.
Each GPU gets its own RNG seed, so any random operations such as dropout or random masking in the training script itself will be different on each GPU, but deterministically so.
The dataloader workers each have distinct seeds from each other for torch, numpy and python's random module, so randomly selected augmentations won't be replicated across workers. (Pytorch only sets up its own worker seeds correctly, leaving numpy and random mirrored across all workers.)

Caution: To get exactly the same model produced when training with the same seed, you will need to run the training script with the --deterministic flag to disable cuDNN's non-deterministic operations and use precisely the same number of GPU devices and CPU workers on each attempt. Without these steps, the model will be almost the same (because the initial seed for the model parameters was the same, and the training trajectory was very similar), but not exactly the same, due to (a) non-deterministic cuDNN operations (b) the batch size increasing with the number of devices (c) any randomized augmentation operations depending on the identity of the CPU worker, which will each have an offset seed.

Prototyping mode, with distinct val/test sets

Initial experiments and hyperparameter searches should be performed without seeing the final test performance. They should be run only on a validation set. Unfortunately, many datasets do not come with a validation set, and it is easy to accidentally use the test set as a validation set, which can lead to overfitting the model selection on the test set.

The image datasets implemented in template_experiment/datasets.py come with support for creating a validation set from the training set, which is separate from the test set. You should use this (with flag --prototyping) during the initial model development steps and for any hyperparameter searches.

Your final models should be trained without --prototyping enabled, so that the full training set is used for training and the best model is produced.

Optional extra package dependencies

There are several requirements files in the root directory of the repository. The idea is the requirements.txt file contains the minimal set of packages that are needed to use the models in the package. The other requirements files are for optional extra packages.

requirements-dev.txt: Extra packages needed for code development (i.e. writing the codebase)
requirements-notebook.txt: Extra packages needed for running the notebooks.
requirements-train.txt: Extra packages needed for training the models.

The setup.py file will automatically parse any requirements files in the root directory of the repository which are named like requirements-*.txt and make them available to pip as extras.

For example, to install the repository to your virtual environment with the extra packages needed for training:

pip install --editable .[train]

You can also install all the extras at once:

pip install --editable .[all]

Or you can install the extras directly from the requirements files:

pip install -r requirements-train.txt

As a developer of the repository, you will need to pip install the package with the --editable flag so the installed copy is updated automatically when you make changes to the codebase.

Automated code checking and formatting

The template repository comes with a pre-commit stack. This is a set of git hooks which are executed every time you make a commit. The hooks catch errors as they occur, and will automatically fix some of these errors.

To set up the pre-commit hooks, run the following code from within the repo directory:

pip install -r requirements-dev.txt
pre-commit install

Whenever you try to commit code which is flagged by the pre-commit hooks, the commit will not go through. Some of the pre-commit hooks (such as black, isort) will automatically modify your code to fix formatting issues. When this happens, you'll have to stage the changes made by the commit hooks and then try your commit again. Other pre-commit hooks, such as flake8, will not modify your code and will just tell you about issues in what you tried to commit (e.g. a variable was declared and never used), and you'll then have to manually fix these yourself before staging the corrected version.

After installing it, the pre-commit stack will run every time you try to make a commit to this repository on that machine. You can also manually run the pre-commit stack on all the files at any time:

pre-commit run --all-files

To force a commit to go through without passing the pre-commit hooks use the --no-verify flag:

git commit --no-verify

The pre-commit stack which comes with the template is highly opinionated, and includes the following operations:

All outputs in Jupyter notebooks are cleared using nbstripout.
Code is reformatted to use the black style. Any code inside docstrings will be formatted to black using blackendocs. All code cells in Jupyter notebooks are also formatted to black using black_nbconvert.
Imports are automatically sorted using isort.
Entries in requirements.txt files are automatically sorted alphabetically.
Several hooks from pre-commit are used to screen for non-language specific git issues, such as incomplete git merges, overly large files being commited to the repo, bugged JSON and YAML files.
JSON files are also prettified automatically to have standardised indentation.

The pre-commit stack will also run on github with one of the action workflows, which ensures the code that is pushed is validated without relying on every contributor installing pre-commit locally.

This development practice of using pre-commit, and standardizing the code-style using black, is popular among leading open-source python projects including numpy, scipy, sklearn, Pillow, and many others.

If you want to use pre-commit, but want to commit outputs in Jupyter notebooks instead of stripping them, simply remove the nbstripout hook from the .pre-commit-config.yaml file and commit that change.

If you don't want to use pre-commit at all, you can uninstall it:

pre-commit uninstall

and purge it (along with black and flake8) from the repository:

git rm .pre-commit-config.yaml .flake8 .github/workflows/pre-commit.yaml
git commit -m "DEV: Remove pre-commit hooks"

Additional features

This template was forked from a more general python template repository.

For more information on the features of the python template repository, see here.

Contributing

Contributions are welcome! If you can see a way to improve this template:

Clone this repo
Create a feature branch
Make your changes in the feature branch
Push your branch and make a pull request

Or to report a bug or request something new, make an issue.

Provide feedback

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Repository files navigation

PyTorch Experiment Template

Creating a git repository using this template

Installation

Vector one-time set-up

Project one-time set-up

Resuming work on an existing project

Executing the example experiment

Set-up

Example commands

Jupyter notebook

Features

Scalable training script

Preemptable

Checkpoints

Log messages

Weights and Biases

Random Number Generator (RNG) state

Prototyping mode, with distinct val/test sets

Optional extra package dependencies

Automated code checking and formatting

Additional features

Contributing

About

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Packages

Contributors 2

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Name		Name	Last commit message	Last commit date
Latest commit History 202 Commits
.github/workflows		.github/workflows
slogs		slogs
slurm		slurm
template_experiment		template_experiment
.flake8		.flake8
.gitattributes		.gitattributes
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.rst		README.rst
pyproject.toml		pyproject.toml
requirements-dev.txt		requirements-dev.txt
requirements-notebook.txt		requirements-notebook.txt
requirements-train.txt		requirements-train.txt
requirements.txt		requirements.txt
setup.py		setup.py

License

scottclowe/pytorch-experiment-template

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PyTorch Experiment Template

Creating a git repository using this template

Installation

Vector one-time set-up

Project one-time set-up

Resuming work on an existing project

Executing the example experiment

Set-up

Example commands

Jupyter notebook

Features

Scalable training script

Preemptable

Checkpoints

Log messages

Weights and Biases

Random Number Generator (RNG) state

Prototyping mode, with distinct val/test sets

Optional extra package dependencies

Automated code checking and formatting

Additional features

Contributing

About

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