fegin

import torch
import torch.nn as nn


def create_child_class(parent_class):
    class MyLora(parent_class):
        def __init__(self, *args, **kwargs):
            super().__init__(*args, **kwargs)
            # Child-specific initialization
            self.scale = nn.Parameter(torch.ones(1))

fegin

"""
This program reproduces an issue where:
- An Op's inputs are all DTensor
- The Op's outputs are all DTensor
- One output is not used by the downstream
- Hence the gradients should be all zeros for the unused output

Run with: torchrun --nnodes 1 --nproc-per-node 4 dtensor_unused_output.py
"""

fegin

import torch
import torch.nn as nn


class SimpleLinear(nn.Module):
    """A simple module with just a linear layer."""

    def __init__(self, dim):
        super().__init__()
        self.linear = nn.Linear(dim, dim)

fegin

'''Solution to the Cartpole problem using Policy Gradients in Tensorflow.'''
# written October 2016 by Sam Greydanus
# inspired by gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5
import numpy as np
import gym
import tensorflow as tf

# hyperparameters
n_obs = 4              # dimensionality of observations
h = 128                # hidden layer neurons

fegin

'''Solves Pong with Policy Gradients in Tensorflow.'''
# written October 2016 by Sam Greydanus
# inspired by karpathy's gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5
import numpy as np
import gym
import tensorflow as tf

# hyperparameters
n_obs = 80 * 80           # dimensionality of observations
h = 200                   # number of hidden layer neurons

fegin

#!/bin/sh
# dropbox service
# Replace with linux users you want to run Dropbox clients for
DROPBOX_USERS="user1 user2"
 
DAEMON=.dropbox-dist/dropbox
 
start() {
    echo "Starting dropbox..."
    for dbuser in $DROPBOX_USERS; do

fegin

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