start new:
tmux
start new with session name:
tmux new -s myname
{0: 'tench, Tinca tinca', | |
1: 'goldfish, Carassius auratus', | |
2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', | |
3: 'tiger shark, Galeocerdo cuvieri', | |
4: 'hammerhead, hammerhead shark', | |
5: 'electric ray, crampfish, numbfish, torpedo', | |
6: 'stingray', | |
7: 'cock', | |
8: 'hen', | |
9: 'ostrich, Struthio camelus', |
As configured in my dotfiles.
start new:
tmux
start new with session name:
L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns
Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs
-- show running queries (pre 9.2) | |
SELECT procpid, age(clock_timestamp(), query_start), usename, current_query | |
FROM pg_stat_activity | |
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' | |
ORDER BY query_start desc; | |
-- show running queries (9.2) | |
SELECT pid, age(clock_timestamp(), query_start), usename, query | |
FROM pg_stat_activity | |
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%' |
""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
import torch | |
import torch.distributed as dist | |
import torch.nn as nn | |
import torch.multiprocessing as mp | |
from torch.nn.parallel import DistributedDataParallel as DDP | |
from fairscale.nn.data_parallel import ShardedDataParallel as ShardedDDP | |
from fairscale.optim.oss import OSS | |
from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP | |
import os |
(Internal Tranining Material)
Usually the first step in performance optimization is to do profiling, e.g. to identify performance hotspots of a workload. This gist tells basic knowledge of performance profiling on PyTorch, you will get:
This tutorial takes one of my recent projects - pssp-transformer as an example to guide you through path of PyTorch CPU peformance optimization. Focus will be on Part 1 & Part 2.
No, seriously, don't. You're probably reading this because you've asked what VPN service to use, and this is the answer.
Note: The content in this post does not apply to using VPN for their intended purpose; that is, as a virtual private (internal) network. It only applies to using it as a glorified proxy, which is what every third-party "VPN provider" does.