ParentHash
ParentStateRoot
ExtrinsicHash
Slot
包含 Fallback 所有 fields
You'll need a machine with at least 24 cores, 512GB RAM, and a 1TB (or bigger) hard drive with 10,000+ IOPS.
$ apt update
$ apt install -y build-essential clang ocl-icd-opencl-dev ocl-icd-libopencl1 jq libhwloc-dev g++ gcc libc6-dev pkg-config aria2
"height","parent_state_root","circulating_fil_v2","vested_fil","mined_fil","burnt_fil","locked_fil_v2","fil_reserve_disbursed" | |
"4154762","bafy2bzacebstn4dpyny73ozxzq6hpnz4yvytix63oslanrcxdkhvjxzwshcti","623284908252744326878977551","450295821935267947743676766","348404017742368660828809424","39928283798894336996226546","152553266587771349927345139","17066618961773405230063046" | |
"4154763","bafy2bzaceckv4uuudwpidkeumk45iburvpmnwxu3xqzzv2lusbq7yphjbuphw","623284981586766250215024455","450295886909432870371784325","348404068754087742681107067","39928283798894618232865446","152553309239633149835064537","17066618961773405230063046" | |
"4154764","bafy2bzacedbhpnl3jw3w33huiicf3m575vdnutlyg2eemnamaf4rjzjyfi3xq","623285046917867302842419582","450295951883597792999891884","348404076041404446675331151","39928283798894778457113346","152553316170013563605753153","17066618961773405230063046" | |
"4154765","bafy2bzaceastgsdb5pa27ft362k77humrqmvdhw2uwb7pzx5ahz3hj2kypmmk","623285109597090009894423847","450296016857762715627999443","34 |
server: | |
http_listen_address: 0.0.0.0 | |
http_listen_port: 9080 | |
positions: | |
filename: /tmp/positions.yaml | |
clients: | |
- url: url | |
curl -X POST \ | |
https://enurl.app/v1/anon_shorten \ | |
-H 'Accept: */*' \ | |
-H 'Cache-Control: no-cache' \ | |
-H 'Connection: keep-alive' \ | |
-H 'Content-Type: application/json' \ | |
-H 'Host: enurl.app' \ | |
-H 'Postman-Token: fee2d748-ebe7-46d7-a2bd-0189567bb524,48e1c73d-c1b3-4eb4-a006-3692a27f9fa8' \ | |
-H 'User-Agent: PostmanRuntime/7.15.0' \ | |
-H 'accept-encoding: gzip, deflate' \ |
class Solution: | |
# 0<= value <= 100 | |
base = 101 | |
# leetcode popular prime number | |
p = 10 ** 9 + 7 | |
def findLength(self, nums1: List[int], nums2: List[int]) -> int: | |
l = 0 | |
r = min(len(nums1), len(nums2)) | |
def forward(self, feature): | |
for f in feature: | |
f = torch.from_numpy(f) | |
f = f.float() | |
lstm_out, self.hidden = self.lstm( | |
f.view(1, 1, len(f)), self.hidden) | |
tag_space = self.hidden2tag(lstm_out.view(1, self.hidden_dim)) | |
tag_scores = F.log_softmax(tag_space, dim=1) |
def lower_df(df): | |
pattern = re.compile('\W+') | |
for field in ['name', 'category_name', 'brand_name', 'item_description']: | |
_str = list(map( | |
lambda x: re.sub(pattern, ' ', x), | |
df[field].str.lower() | |
)) | |
df[field] = _str |