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# Core dependencies
gymnasium==1.2.1
prettytable
tqdm
tyro
torch>=2.7.0
warp-lang
mujoco-warp @ git+https://github.com/google-deepmind/mujoco_warp@e605c406363805d949b75d50dd5ffb3d79bc3f70
mujoco
trimesh>=4.8.3
Package Version Editable project location
------------------------- ------------ ----------------------------------
absl-py 2.3.1
annotated-types 0.7.0
anyio 4.11.0
attrs 25.4.0
certifi 2025.10.5
charset-normalizer 3.4.4
click 8.3.0
cloudpickle 3.1.2
@YuehChuan
YuehChuan / gist:727e11019449b73513be5116296db022
Last active April 19, 2025 08:39
DeepSeek-R1 FUll version on 16X H100
export GLOO_SOCKET_IFNAME=net1
python3 -m sglang.launch_server --model <model_name> --port 8000 --tp 16 --dist-init-addr <node1_ip>:6379 --nnodes 2 --node-rank 0 --trust-remote-code --host 0.0.0.0
python3 -m sglang.launch_server --model <model_name> --port 8000 --tp 16 --dist-init-addr <node1_ip>:6379 --nnodes 2 --node-rank 0 --trust-remote-code --host 0.0.0.0
export GLOO_SOCKET_IFNAME=<ifname>
python3 -m sglang.launch_server --model <model_name> --port 8000 --tp 16 --dist-init-addr <node1_ip>:6379 --nnodes 2 --node-rank 1 --trust-remote-code --host 0.0.0.0
@YuehChuan
YuehChuan / gist:1daab7c49828d925294e4dd9eb813e42
Created October 27, 2023 00:04
nn.embedding backward grad
import torch
import torch.nn as nn
# an Embedding module containing 10 tensors of size 3
embedding = nn.Embedding(6, 3)
# a batch of 2 samples of 3 elements each
input = torch.LongTensor([[1, 2, 3],[0,2,3]])
loss=torch.sum(embedding(input))
print("embedding" )
print(embedding.weight )
@YuehChuan
YuehChuan / CMakeLists.txt
Created April 21, 2021 13:55 — forked from tdenewiler/CMakeLists.txt
ROS Synchronization Example
cmake_minimum_required(VERSION 2.8.3)
project(sync_example)
find_package(catkin REQUIRED COMPONENTS message_filters roscpp sensor_msgs)
catkin_package(
CATKIN_DEPENDS message_filters roscpp sensor_msgs
)
include_directories(${catkin_INCLUDE_DIRS})
@YuehChuan
YuehChuan / imagenet_normalization.py
Created November 10, 2020 07:29 — forked from pmeier/imagenet_normalization.py
Calculation of ImageNet z-score parameters
import argparse
import multiprocessing
from math import ceil
import torch
from torch.utils import data
from torchvision import datasets, transforms
class FiniteRandomSampler(data.Sampler):
def __init__(self, data_source, num_samples):
@YuehChuan
YuehChuan / PyTorch 入門最速傳說.ipynb
Created July 10, 2020 07:53 — forked from remorsecs/PyTorch 入門最速傳說.ipynb
本文目標是讓沒寫過 PyTorch 的讀者成為 PyTorch 新手 (?)
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#!/bin/sh
printf "\n$(cat ascii.txt)\n"
@YuehChuan
YuehChuan / ascii.txt
Created May 31, 2020 07:15
ascii.txt
       
   .....   
   .........   
     .  ..  ....   
     .  ..  ....     
   .  .............     
   ..[
#include <stdio.h>
#include <stdlib.h>
//function pointer
typedef int (*MathMethod)(int, int);
int Mul(int a, int b) { return a*b; }
float Divide(int a, int b) { return a/b; }
int Minus(int a, int b) { return a-b; }
int Add(int a, int b) { return a+b; }