Qp

avalanche/training/plugins/gem.py

@@ -1,6 +1,6 @@
 from typing import Dict
 import numpy as np
-import quadprog
+import qpsolvers
 import torch
 from torch import Tensor
 from torch.utils.data import DataLoader
@@ -171,7 +171,10 @@ def solve_quadprog(self, g):
         q = np.dot(memories_np, gradient_np) * -1
         G = np.eye(t)
         h = np.zeros(t) + self.memory_strength
-        v = quadprog.solve_qp(P, q, G, h)[0]
+        # solution with old quadprog library, same as the author's implementation
+        # v = quadprog.solve_qp(P, q, G, h)[0]
+        # using new library qpsolvers
+        v = qpsolvers.solve_qp(P=P, q=-q, G=-G.transpose(), h=-h, solver="quadprog")
         v_star = np.dot(v, memories_np) + gradient_np
 
         return torch.from_numpy(v_star).float()

docker/Dockerfile_dev

@@ -1,17 +1,6 @@
 # syntax=docker/dockerfile:1
 
 FROM nvcr.io/nvidia/pytorch:23.09-py3
-
-# Needed for quadprog
-RUN apt-get update && apt-get install -y \
-  build-essential \
-  && rm -rf /var/lib/apt/lists/*
-
 ADD requirements.txt .
-
-# RUN pip install virtualenv
-# RUN python -m venv local_venv
-# RUN source local_venv/bin/activate
 RUN pip install -r requirements.txt
-
 SHELL ["/bin/bash", "--login", "-c"]

docker/nightly/Dockerfile

@@ -1,13 +1,6 @@
 # syntax=docker/dockerfile:1
 
 FROM continualai/avalanche-test-3.9:latest
-
-# Needed for quadprog
-RUN apt-get update && apt-get install -y \
-  build-essential \
-  && rm -rf /var/lib/apt/lists/*
-
 RUN echo "conda activate avalanche-env" >> ~/.bashrc
 SHELL ["/bin/bash", "--login", "-c"]
-
 RUN pip install git+https://github.com/ContinualAI/avalanche.git

requirements.txt

@@ -14,4 +14,4 @@ torchmetrics
 gdown
 dill
 packaging
-quadprog @ git+https://github.com/HKaras/quadprog.git@690081f3285d4eb554394209473a274f230cd9ba
+qpsolvers