vadimkantorov

nvidia-smi -q -x | grep "</pid>" | tr -d "</pid>\t"

vadimkantorov

# https://github.com/pytorch/pytorch/issues/158710
# https://github.com/pytorch/pytorch/issues/158698
# https://github.com/pytorch/pytorch/issues/69431

import torch

def to_(tensor1d, dtype, *, chunks = 0, split_size = 0):
    # TODO: instead of clone() maybe could copy_ into a buffer, clone() does not allow using a buffer
    # TODO: unclear if these codes can support autograd, and if so, will it remember too much in saved_for_backward 
    

vadimkantorov

# https://en.wikipedia.org/wiki/Base64
# 00123456  00ABCDEF  00abcdef  00uvwxyz
# 123456AB  CDEFabcd  efuvwxyz
# this code does not support batches. adapting for e.g. concatenated varlen format is possible, but need to handle/preserve varlen information and paddings in some way

import torch

def base64_encode_padded(input_as_uint8_tensor):
    base64_alphabet, base64_pad = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/', '='
    encode = torch.tensor(list(map(ord, base64_alphabet)), dtype = torch.uint8, device = input_as_uint8_tensor.device)

vadimkantorov

# Nebius s3
# sudo apt-get install s3cmd # export the vars below or configure access_key / secret_key
# export AWS_ACCESS_KEY_ID=...
# export AWS_SECRET_ACCESS_KEY=...
# https://s3tools.org/usage
# s3cmd -c ~/.nebius.s3cfg ls

[default]
# access_key=...
# secret_key=...

vadimkantorov

# PYTHONPATH=. python ...

import os
import torch

def cuda_oom_hook(device, alloc, device_alloc, device_free, info = dict(counter = 0), snapshot_dump_file_pattern = './memory_snapshot_{pid}_{oom_counter}.pt'):
    memory_summary = torch.cuda.memory_summary(device = device)
    memory_snapshot = torch.cuda.memory._snapshot(device = device)
    pid = os.getpid()
    print('device:', device, 'oom#:', info['oom_counter'], 'pid:', pid, 'alloc:', alloc, 'device_alloc:', device_alloc, 'device_free:', device_free)

vadimkantorov

import multiprocessing
import itertools

inputs = list(range(111))
batchsize = 10
num_workers = 4

batches = itertools.batched(inputs, batchsize)

def reducer(xs):

vadimkantorov

# Usage: bash cache_hf_model.sh Qwen/Qwen3-8B
# export HF_HOME=/my/cache/HF_HOME
python -c 'import sys, transformers; transformers.AutoModel.from_pretrained(sys.argv[-1], trust_remote_code=True, device_map="meta")' $@

vadimkantorov

def load_dotenv(dotenv_path = '.env'):
    # https://snarky.ca/use-toml-for-env-files/
    # https://github.com/theskumar/python-dotenv
    '''
    # such simple key-value files are toml subset and can be read via tomllib without external packages or hacks
    a="b"
    c="d"
    '''
    import os, tomllib
    os.environ.update(tomllib.load(open(dotenv_path, 'rb')))

vadimkantorov

import sys, fsspec
with fsspec.open(sys.argv[1], 'rt') as f: # must pass 'rt' explicitly, as in fsspec the default mode is 'rb'
    print(f.read()) # msut use context manager as in fsspec the result of fsspec.open(...) does not have method read()

# echo world > hello.txt
# python catfsspec.py hello.txt
# python catfsspec.py file://hello.txt
# python catfsspec.py s3://mybucket/hello.txt

vadimkantorov

# reference: https://gist.github.com/0xjac/85097472043b697ab57ba1b1c7530274

git clone --bare [email protected]:volcengine/verl.git
cd verl.git

# create a bare repo vadimkantorov/verl
git push --mirror [email protected]:vadimkantorov/verl.git
cd .. && rm -rf verl.git

# set up upstream remote