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Running SWE-Agent with Apptainer

Notes on how to use SWE-Agent with Apptainer (because for some HPC, docker is not available).

Running single task runs

Like a DockerFile, we can configure a container using a definition file. Save the snippet below as sweagent.def.

Bootstrap: docker
From: python:3.12

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# Model dir to save logs, ckpts, etc. in "gs://model_dir" format.
MODEL_DIR="gs://bigscience/experiment_d/multilingual_t0/test-run/model"

# Data dir to save the processed dataset in "gs://data_dir" format.
TFDS_DATA_DIR="gs://bigscience/experiment_d/multilingual_t0/test-run/data"
T5X_DIR=${HOME}"/t5x"  # directory where the T5X repo is cloned.

python3 ${T5X_DIR}/t5x/train.py \
  --gin_file=${T5X_DIR}"/t5x/examples/t5/t5_1_1/examples/t5_1_1_base_wmt_finetune.gin" \
  --gin.MODEL_DIR="'${MODEL_DIR}'" \

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git clone --branch=main https://github.com/google-research/t5x
cd t5x

python3 -m pip install -e .

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# Run flax mnist example (optional)
pip install --user tensorflow-datasets==3.1.0 ml_collections clu
git clone https://github.com/google/flax.git
pip install --user -e flax
cd flax/examples/mnist
mkdir /tmp/mnist
python3 main.py --workdir=/tmp/mnist --config=configs/default.py --config.learning_rate=0.05 --config.num_epochs=5

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sudo apt-add-repository -r ppa:graphics-drivers/ppa
sudo apt update
sudo apt remove nvidia*
sudo apt autoremove
sudo apt-get install ubuntu-drivers-common
sudo ubuntu-drivers autoinstall
# sudo apt -f install

sudo apt install aptitude
sudo aptitude install nvidia-440

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# Instructions for installing GCC 4.9 on various platforms.
# The commands show instructions for GCC 4.9, but any higher version will also work!

# Ubuntu (https://askubuntu.com/questions/466651/how-do-i-use-the-latest-gcc-on-ubuntu/581497#581497)
sudo apt-get install software-properties-common
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-4.9 g++-4.9
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.9 60 --slave /usr/bin/g++ g++ /usr/bin/g++-4.9

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import torch
import torch.nn.functional as F

# Inspired from Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
# https://arxiv.org/pdf/1603.05201.pdf

class CReLU(nn.Module):

    def __init__(self, inplace=False):
        super(CReLU, self).__init__()

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import json
import base64

IMAGE_FILE = "example.png"

with open(IMAGE_FILE, "rb") as image_file:
  encoded_string = base64.b64encode(image_file.read())

base64_string = encoded_string.decode('utf-8')
json_data = dumps({'image': base64_string}, indent=2)