mirror of
https://github.com/vacp2p/wakurtosis.git
synced 2026-01-08 22:38:04 -05:00
495fe8cd84
* make regular graphs the default * added regular graph * network and degree distribution plots * log network type * handle n*d odd * set fanout to 6 * title/ylable * added --num-nodes --fanout to title * remove prints in draw * avg degree, float format * config.json
617 lines
23 KiB
Python
617 lines
23 KiB
Python
#! /usr/bin/env python3
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import matplotlib.pyplot as plt
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import networkx as nx
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import numpy as np
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import random, math
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import sys, os, shutil
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import json, ast
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from collections import defaultdict
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from pathlib import Path
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import time, tracemalloc
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import string
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import typer
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import yaml
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import hashlib
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from enum import Enum, EnumMeta
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from blspy import PrivateKey, Util, BasicSchemeMPL, G2Element, G1Element
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# Enums & Consts
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class MetaEnum(EnumMeta):
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def __contains__(cls, item):
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try:
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cls(item)
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except ValueError:
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return False
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return True
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class BaseEnum(Enum, metaclass=MetaEnum):
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pass
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class Trait(BaseEnum):
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NWAKU = "nwaku"
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GOWAKU = "gowaku"
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DISCV5 = "discv5"
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DNSDISC = "dnsdisc"
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DNS = "dns"
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FLTER = "flter"
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LIGHTPUSH = "lightpush"
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METRICS = "metrics"
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NODE = "node"
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PEER = "peer"
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PEERXCHNG = "peerxchng"
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RELAY = "relay"
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REST = "rest"
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RLN = "rln"
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RPC = "rpc"
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STORE = "store"
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SWAP = "swap"
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WEBSOCKET = "websocket"
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NOMOS = "nomos"
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# To add a new node type, add appropriate entries to the nodeType and nodeTypeToDocker
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class nodeType(BaseEnum):
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NWAKU = "nwaku" # waku desktop config
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GOWAKU = "gowaku" # waku mobile config
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NOMOS = "nomos"
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nodeTypeToDocker = {
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nodeType.NWAKU: "nim-waku",
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nodeType.GOWAKU: "go-waku",
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nodeType.NOMOS: "nomos"
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}
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# To add a new network type, add appropriate entries to the networkType and networkTypeSwitch
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# the networkTypeSwitch is placed before generate_network(): fwd declaration mismatch with typer/python :/
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class networkType(Enum):
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CONFIGMODEL = "configmodel"
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SCALEFREE = "scalefree" # power law
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NEWMANWATTSSTROGATZ = "newmanwattsstrogatz" # mesh, smallworld
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BARBELL = "barbell" # partition
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BALANCEDTREE = "balancedtree" # committees?
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NOMOSTREE = "nomostree" # balanced binary tree with even # of leaves
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STAR = "star" # spof
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REGULAR = "regular" # gossip-sub / waku
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NW_DATA_FNAME = "network_data.json"
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NODES_JSON, NODE_PREFIX, SUBNET_PREFIX, CONTAINERS_JSON, CONTAINER_PREFIX = \
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"nodes", "node", "subnetwork", "containers", "containers"
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ID_STR_SEPARATOR = "-"
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### I/O related fns ##############################################################
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# Dump to a json file
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def write_json(dirname, json_dump):
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fname = os.path.join(dirname, NW_DATA_FNAME)
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with open(fname, "w") as f:
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json.dump(json_dump, f, indent=2)
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def write_toml(dirname, node_name, toml):
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fname = os.path.join(dirname, f"{node_name}.toml")
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with open(fname, "w") as f:
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f.write(toml)
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# Draw the network and output the image to a file; does not account for subnets yet
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def draw_network(ctx, dirname, H):
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fname = os.path.join(dirname, NW_DATA_FNAME)
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fig, axes = plt.subplots(1, 2, layout='constrained', sharey=False)
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fig.set_figwidth(12)
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fig.set_figheight(10)
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n = len(H.nodes)
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e = len(H.edges)
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s = 0
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for i in H.nodes:
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s += H.degree[i]
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avg = 2 * e/n
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axes[0].set_title(f'The Generated Network: num-nodes = {n}, avg degree= {avg:.2f}')
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nx.draw(H, ax=axes[0], pos=nx.kamada_kawai_layout(H), with_labels=True)
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degree_sequence = sorted((d for n, d in H.degree()), reverse=True)
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deg, cnt = *np.unique(degree_sequence, return_counts=True),
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normalised_cnt = cnt/np.array(n)
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axes[1].bar(deg, normalised_cnt, align='center',
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width=0.9975, edgecolor='k', facecolor='green', alpha=0.5)
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axes[1].set_xticks(range(max(degree_sequence)+1))
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axes[1].set_title(f'Normalised Degree Histogram: fanout = {ctx.params["fanout"]}')
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axes[1].set_xlabel("Degree")
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axes[1].set_ylabel("Fraction of Nodes")
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plt.savefig(f'{os.path.splitext(fname)[0]}.png', format="png", bbox_inches="tight")
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plt.show()
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# Has trouble with non-integer/non-hashable keys
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def read_json(fname):
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with open(fname) as f:
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jdata = json.load(f)
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return nx.node_link_graph(jdata)
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# check if the required dir can be created
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def exists_or_nonempty(dirname):
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if not os.path.exists(dirname):
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return False
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elif not os.path.isfile(dirname) and os.listdir(dirname):
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print(f"{dirname}: exists and not empty")
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return True
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elif os.path.isfile(dirname):
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print(f"{dirname}: exists but not a directory")
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return True
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else:
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return False
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# remove mkdir
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def make_empty_dir(dirname):
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if os.path.isdir(dirname):
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shutil.rmtree(dirname)
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os.makedirs(dirname, exist_ok=True)
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### topics related fns #############################################################
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# Generate a random string of upper case chars
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def generate_random_string(n):
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return "".join(random.choice(string.ascii_uppercase) for _ in range(n))
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# Generate the topics - topic followed by random UC chars - Eg, topic_XY"
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def generate_topics(num_topics):
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topic_len = int(math.log(num_topics) / math.log(26)) + 1 # base is 26 - upper case letters
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topics = {i: f"topic_{generate_random_string(topic_len)}" for i in range(num_topics)}
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return topics
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# Get a random sub-list of topics
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def get_random_sublist(topics):
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n = len(topics)
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lo = random.randint(0, n - 1)
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hi = random.randint(lo + 1, n)
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sublist = []
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for i in range(lo, hi):
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sublist.append(topics[i])
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return sublist
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### network processing related fns #################################################
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# Network Types
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# |G| = n
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def generate_config_model(ctx):
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n = ctx.params["num_nodes"]
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# degrees = nx.random_powerlaw_tree_sequence(n, tries=10000)
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degrees = [random.randint(1, n) for i in range(n)]
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if (sum(degrees)) % 2 != 0: # adjust the degree to be even
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degrees[-1] += 1
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return nx.configuration_model(degrees) # generate the graph
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# |G| = n
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def generate_scalefree_graph(ctx):
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n = ctx.params["num_nodes"]
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return nx.scale_free_graph(n)
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# |G| = n; n must be larger than k=D=3
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def generate_newmanwattsstrogatz_graph(ctx):
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n = ctx.params["num_nodes"]
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fanout = ctx.params["fanout"]
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return nx.newman_watts_strogatz_graph(n, fanout, 0.5)
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# |G| = n (if odd); n+1 (if even)
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def generate_barbell_graph(ctx):
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n = ctx.params["num_nodes"]
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return nx.barbell_graph(int(n / 2), 1)
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# |G| > fanout^{\floor{log_n} + 1}
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def generate_balanced_tree(ctx):
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n = ctx.params["num_nodes"]
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fanout = ctx.params["fanout"]
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height = int(math.log(n) / math.log(fanout))
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return nx.balanced_tree(fanout, height)
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# nomostree is a balanced binary tree with even number of leaves
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# |G| = n (if odd); n+1 (if even)
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def generate_nomos_tree(ctx):
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n = ctx.params["num_nodes"]
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fanout = ctx.params["fanout"]
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# nomos currently insists on binary trees
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assert(fanout == 2)
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height = int(math.log(n) / math.log(fanout))
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G = nx.balanced_tree(fanout, height)
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i, diff = 0, G.number_of_nodes() - n
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leaves = [x for x in G.nodes() if G.degree(x) == 1]
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nleaves = len(leaves)
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if (nleaves - diff) % 2 != 0:
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diff -= 1
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for node in leaves:
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if i == diff:
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break
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G.remove_node(node)
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i += 1
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G = nx.convert_node_labels_to_integers(G)
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return G
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# |G| = n
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def generate_star_graph(ctx):
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n = ctx.params["num_nodes"]
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return nx.star_graph(n-1)
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# |G| = n, if n*d is even; n+1 if n*d is odd
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def generate_regular_graph(ctx):
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d = ctx.params["fanout"]
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n = ctx.params["num_nodes"]
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return nx.random_regular_graph(d, n+1) if n*d % 2 else nx.random_regular_graph(d, n)
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networkTypeSwitch = {
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networkType.CONFIGMODEL: generate_config_model,
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networkType.SCALEFREE: generate_scalefree_graph,
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networkType.NEWMANWATTSSTROGATZ: generate_newmanwattsstrogatz_graph,
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networkType.BARBELL: generate_barbell_graph,
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networkType.BALANCEDTREE: generate_balanced_tree,
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networkType.NOMOSTREE: generate_nomos_tree,
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networkType.STAR: generate_star_graph,
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networkType.REGULAR: generate_regular_graph
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}
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# Generate the network from nw type
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def generate_network(ctx):
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network_type = networkType(ctx.params["network_type"])
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return postprocess_network(networkTypeSwitch.get(network_type)(ctx))
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# Label the generated network with prefix
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def postprocess_network(G):
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G = nx.Graph(G) # prune out parallel/multi edges
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G.remove_edges_from(nx.selfloop_edges(G)) # remove the self-loops
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mapping = {i : f"{NODE_PREFIX}{ID_STR_SEPARATOR}{i}" for i in range(len(G))}
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return nx.relabel_nodes(G, mapping) # label the nodes
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def generate_subnets(G, num_subnets):
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n = len(G.nodes)
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if num_subnets == n: # if num_subnets == size of the network
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return {f"{NODE_PREFIX}{ID_STR_SEPARATOR}{i}" : f"{SUBNET_PREFIX}_{i}" for i in range(n)}
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# Permute the node indices; this makes sure that the nodes are assigned randomly to subnets
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lst = list(range(n))
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random.shuffle(lst)
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# Select (without replacement) a num_subnets - 1 of offsets; make sure final offset is n-1.
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# Each offset demarcates a subnet boundary
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offsets = sorted(random.sample(range(0, n), num_subnets - 1))
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offsets.append(n - 1) # we have num_subnets offsets
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start, subnet_id, node2subnet = 0, 0, {}
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for end in offsets:
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# Build a node2subnet map as follows
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# From the permuted lst, pick nodes whose indices are in the closed interval [start, end].
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# Remember, these are *sorted* offsets in the range of 0..n and without replacement; so
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# they will all index correctly.
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# Finally, assign all these node to the current subnet.
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for i in range(start, end + 1):
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node2subnet[f"{NODE_PREFIX}{ID_STR_SEPARATOR}{lst[i]}"] = f"{SUBNET_PREFIX}_{subnet_id}"
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#node2subnet[lst[i]] = subnet_id
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start = end # roll over the start to the end of the last offset
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subnet_id += 1 # increment the subnet_id
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return node2subnet
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### file format related fns ###########################################################
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# Generate per node toml configs
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def generate_toml(traits_dir, topics, traits_list):
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topics, node_type, tomls = get_random_sublist(topics), traits_list[0], ""
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if node_type == nodeType.GOWAKU: # comma separated list of quoted topics
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topic_str = ", ".join(f"\"{t}\"" for t in topics)
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topic_str = f"[{topic_str}]"
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else: # space separated topics
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topic_str = " ".join(topics)
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topic_str = f"\"{topic_str}\""
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for trait in traits_list[1:]: # skip the first trait as it is docker/node selector.
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toml = f'#{trait}\n'
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tomlf = f"{traits_dir}/{trait}.toml"
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if not os.path.isfile(tomlf):
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raise ValueError(f"traits: missing trait file {tomlf}")
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with open(tomlf, 'rb') as f:
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strlines = [l.decode("utf-8").strip() for l in f if not len(l.split()) == 0]
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toml += ''.join([f'{l}\n' for l in strlines if not l.startswith('#')])
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tomls += toml + '\n'
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return f"{tomls}#topics\ntopics = {topic_str}\n"
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# Convert a dict to pair of arrays
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def dict_to_arrays(dic):
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keys, vals = zip(*dic.items())
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return keys, vals
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# Check for range failures in a list
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def range_fails(lst, min=0, max=100):
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return any(x < min or x > max for x in lst)
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# Check for sum failures in a list
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def sum_fails(lst, sum_expected=100):
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return not sum(lst) == sum_expected
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# Construct the nodeType from the trait
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def traits_to_nodeType(s):
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return nodeType(s.split(':')[0])
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# Validate the traits distribution (stick to percentages: num nodes may vary post generation)
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def validate_traits_distribution(traits_dir, traits_distribution):
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traits, traits_freq = dict_to_arrays(traits_distribution)
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if range_fails(traits_freq, max=100):
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raise ValueError(f"{traits_distribution} : invalid percentage (>{100} or <0)")
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if sum_fails(traits_freq, sum_expected=100):
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raise ValueError(f"{traits_distribution} : percentages do not sum to {100}")
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if not os.path.exists(traits_dir):
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raise ValueError(f"{traits_dir} : trait directory does not exist!")
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for s in traits:
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traits_list = s.split(":")
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if traits_list[0] not in nodeType:
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raise ValueError(f"{traits_distribution} : unknown node type {traits_list[0]} in {s}")
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for t in traits_list[1:]:
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if t not in Trait and not (os.path.exists(f"{traits_dir}/{t}.toml") or os.path.exists(f"{traits_dir}/{t}.yml")):
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raise ValueError(f"{traits_distribution} : unknown trait {t} in {s}")
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# Generate a list of nodeType enums that respects the node type distribution
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def generate_traits_distribution(node_type_distribution, G):
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num_nodes = G.number_of_nodes()
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nodes, node_percentage = dict_to_arrays(node_type_distribution)
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traits_distribution = []
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for i, n in enumerate(nodes):
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traits_distribution += [nodes[i]] * math.ceil(node_percentage[i] * num_nodes/100)
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random.shuffle(traits_distribution)
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return traits_distribution
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# Inverts a dictionary of lists (of lists/tuples)
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def invert_dict_of_list(d, idx=0):
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inv = defaultdict(list)
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for key, val in d.items():
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inv[val[idx]].append(key)
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return inv
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# TODO: reduce container packer memory consumption
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# Packs the nodes into container in a subnet aware manner : optimal
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# Number of containers =
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# $$ O(\sum_{i=0}^{num_subnets} log_{container_size}(#Nodes_{numsubnets}) + num_subnets)
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def pack_nodes(container_size, node2subnet):
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subnet2nodes = invert_dict_of_list(node2subnet)
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port_shift, cid, node2container = 0, 0, {}
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for subnet in subnet2nodes:
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for node in subnet2nodes[subnet]:
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if port_shift >= container_size:
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port_shift, cid = 0, cid+1
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node2container[node] = (port_shift, f"{CONTAINER_PREFIX}_{cid}")
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port_shift += 1
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return node2container
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# Generates network-wide json and per-node toml and writes them
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def generate_and_write_files(ctx: typer, G):
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topics = generate_topics(ctx.params["num_topics"])
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node2subnet = generate_subnets(G, ctx.params["num_subnets"])
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traits_distribution = generate_traits_distribution(ctx.params["node_type_distribution"], G)
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node2container = pack_nodes(ctx.params["container_size"], node2subnet)
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container2nodes = invert_dict_of_list(node2container, 1)
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json_dump, json_dump[CONTAINERS_JSON], json_dump[NODES_JSON] = {}, {}, {}
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for container, nodes in container2nodes.items():
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json_dump[CONTAINERS_JSON][container] = nodes
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i, traits_dir = 0, ctx.params["traits_dir"]
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for node in G.nodes:
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# write the per node toml for the i^ith node of appropriate type
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traits_list, i = traits_distribution[i].split(":"), i + 1
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node_type = nodeType(traits_list[0])
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write_toml(ctx.params["output_dir"], node, generate_toml(traits_dir, topics, traits_list))
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json_dump[NODES_JSON][node] = {}
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json_dump[NODES_JSON][node]["static_nodes"] = []
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for edge in G.edges(node):
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json_dump[NODES_JSON][node]["static_nodes"].append(edge[1])
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json_dump[NODES_JSON][node][SUBNET_PREFIX] = node2subnet[node]
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json_dump[NODES_JSON][node]["image"] = nodeTypeToDocker.get(node_type)
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# the per node tomls will continue for now as they include topics
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json_dump[NODES_JSON][node]["node_config"] = f"{node}.toml"
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# logs ought to continue as they need to be unique
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json_dump[NODES_JSON][node]["node_log"] = f"{node}.log"
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port_shift, cid = node2container[node]
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json_dump[NODES_JSON][node]["port_shift"] = port_shift
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json_dump[NODES_JSON][node]["container_id"] = cid
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write_json(ctx.params["output_dir"], json_dump) # network wide json
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def generate_and_write_files_nomos(ctx: typer, G):
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node2subnet = generate_subnets(G, ctx.params["num_subnets"])
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node2container = pack_nodes(ctx.params["container_size"], node2subnet)
|
|
container2nodes = invert_dict_of_list(node2container, 1)
|
|
|
|
json_dump, json_dump[CONTAINERS_JSON], json_dump[NODES_JSON] = {}, {}, {}
|
|
for container, nodes in container2nodes.items():
|
|
json_dump[CONTAINERS_JSON][container] = nodes
|
|
|
|
# TODO Put in json_dump[PUBLIC_KEYS] all public keys
|
|
json_dump["all_public_keys"] = []
|
|
|
|
for node in G.nodes:
|
|
json_dump[NODES_JSON][node] = {}
|
|
json_dump[NODES_JSON][node]["static_nodes"] = []
|
|
for edge in G.edges(node):
|
|
json_dump[NODES_JSON][node]["static_nodes"].append(edge[1])
|
|
json_dump[NODES_JSON][node][SUBNET_PREFIX] = node2subnet[node]
|
|
json_dump[NODES_JSON][node]["image"] = nodeTypeToDocker.get(nodeType.NOMOS)
|
|
# the per node tomls will continue for now as they include topics
|
|
json_dump[NODES_JSON][node]["node_config"] = f"{node}.yml"
|
|
# logs ought to continue as they need to be unique
|
|
json_dump[NODES_JSON][node]["node_log"] = f"{node}.log"
|
|
port_shift, cid = node2container[node]
|
|
json_dump[NODES_JSON][node]["container_id"] = cid
|
|
# TODO put in json_dump[NODES_JSON]
|
|
seed = bytes([random.randint(0, 255) for _ in range(32)])
|
|
privatekey = BasicSchemeMPL.key_gen(seed)
|
|
json_dump[NODES_JSON][node]["private_key"] = list(bytes(privatekey))
|
|
#publickey = privatekey.get_g1()
|
|
#hashed_publickey = hashlib.blake2b(bytes(publickey), digest_size=32).digest()
|
|
#json_dump[NODES_JSON][node]["public_key"] = list(hashed_publickey)
|
|
#json_dump["all_public_keys"].append(list(hashed_publickey))
|
|
json_dump[NODES_JSON][node]["public_key"] = list(bytes(privatekey))
|
|
json_dump["all_public_keys"].append(list(bytes(privatekey)))
|
|
|
|
write_ymls(json_dump)
|
|
|
|
# TODO modificar yml para cada nodo con las claves.
|
|
write_json(ctx.params["output_dir"], json_dump) # network wide json
|
|
# shutil.copy2("/config/traits/nomos.yml", "/gennet/network_data/nomos.yml")
|
|
|
|
|
|
def write_ymls(json_dump):
|
|
with open("/config/traits/nomos.yml", "r") as stream:
|
|
try:
|
|
nomos_yml_template = yaml.safe_load(stream)
|
|
except yaml.YAMLError as exc:
|
|
print(exc)
|
|
|
|
for node_name, node_info in json_dump[NODES_JSON].items():
|
|
nomos_yml_template['consensus']['private_key'] = node_info["private_key"]
|
|
nomos_yml_template['consensus']['overlay_settings']['nodes'] = json_dump["all_public_keys"]
|
|
with open(f'/gennet/network_data/{node_name}.yml', 'w') as f:
|
|
yaml.dump(nomos_yml_template, f)
|
|
|
|
|
|
# sanity check : valid json with "gennet" config
|
|
def _config_file_callback(ctx: typer.Context, param: typer.CallbackParam, cfile: str):
|
|
if cfile:
|
|
typer.echo(f"Loading config file: {os.path.basename(cfile)}")
|
|
ctx.default_map = ctx.default_map or {} # Init the default map
|
|
try:
|
|
with open(cfile, 'r') as f: # Load config file
|
|
conf = json.load(f)
|
|
if "gennet" not in conf:
|
|
print(
|
|
f"Gennet configuration not found in {cfile}. Skipping network generation.")
|
|
sys.exit(0)
|
|
if "general" in conf and "prng_seed" in conf["general"]:
|
|
conf["gennet"]["prng_seed"] = conf["general"]["prng_seed"]
|
|
# TODO : type-check and sanity-check the values in config.json
|
|
ctx.default_map.update(conf["gennet"]) # Merge config and default_map
|
|
except Exception as ex:
|
|
raise typer.BadParameter(str(ex))
|
|
return cfile
|
|
|
|
|
|
# sanity check : num_partitions == 1
|
|
def _num_partitions_callback(num_partitions: int):
|
|
if num_partitions > 1:
|
|
raise ValueError(
|
|
f"--num-partitions {num_partitions}, Sorry, we do not yet support partitions")
|
|
return num_partitions
|
|
|
|
|
|
# sanity check : num_subnets < num_nodes
|
|
def _num_subnets_callback(ctx: typer, Context, num_subnets: int):
|
|
num_nodes = ctx.params["num_nodes"]
|
|
if num_subnets == -1:
|
|
num_subnets = num_nodes
|
|
if num_subnets > num_nodes:
|
|
raise ValueError(
|
|
f"num_subnets must be <= num_nodes : num_subnets={num_subnets}, num_nodes={1}")
|
|
return num_subnets
|
|
|
|
|
|
def main(ctx: typer.Context,
|
|
|
|
benchmark: bool = typer.Option(False,
|
|
help="Measure CPU/Mem usage of Gennet"),
|
|
draw: bool = typer.Option(False,
|
|
help="Draw the generated network"),
|
|
container_size: int = typer.Option(1,
|
|
help="Set the number of nodes per container"),
|
|
output_dir: str = typer.Option("network_data",
|
|
help="Set the output directory for Gennet generated files"),
|
|
prng_seed: int = typer.Option(1,
|
|
help="Set the random seed"),
|
|
num_nodes: int = typer.Option(4,
|
|
help="Set the number of nodes"),
|
|
num_topics: int = typer.Option(1,
|
|
help="Set the number of topics"),
|
|
fanout: int = typer.Option(3,
|
|
help="Set the arity for trees, d-regular graphs & newmanwattsstrogatz"),
|
|
node_type_distribution: str = typer.Argument("{\"nwaku\" : 100 }",
|
|
callback=ast.literal_eval, help="Set the node type distribution"),
|
|
network_type: networkType = typer.Option(networkType.REGULAR.value,
|
|
help="Set the node type"),
|
|
num_subnets: int = typer.Option(1, callback=_num_subnets_callback,
|
|
help="Set the number of subnets"),
|
|
num_partitions: int = typer.Option(1, callback=_num_partitions_callback,
|
|
help="Set the number of network partitions"),
|
|
config_file: str = typer.Option("", callback=_config_file_callback, is_eager=True,
|
|
help="Set the input config file (JSON)"),
|
|
traits_dir: Path = typer.Option("./traits", exists=True, file_okay=False,
|
|
dir_okay=True, readable=True, resolve_path=True, help="Set the traits directory")):
|
|
|
|
# Benchmarking: record start time and start tracing mallocs
|
|
if benchmark:
|
|
tracemalloc.start()
|
|
start = time.time()
|
|
|
|
# set the random seed : networkx uses numpy.random as well
|
|
print("Setting the random seed to ", prng_seed)
|
|
random.seed(prng_seed)
|
|
np.random.seed(prng_seed)
|
|
|
|
# validate node type distribution
|
|
validate_traits_distribution(ctx.params["traits_dir"], node_type_distribution)
|
|
|
|
# Generate the network
|
|
# G = generate_network(num_nodes, networkType(network_type), tree_arity)
|
|
G = generate_network(ctx)
|
|
|
|
# Do not complain if the folder already exists
|
|
make_empty_dir(output_dir)
|
|
|
|
# Generate file format specific data structs and write the files
|
|
if node_type_distribution["nomos"] > 0:
|
|
generate_and_write_files_nomos(ctx, G)
|
|
else:
|
|
generate_and_write_files(ctx, G)
|
|
|
|
# Draw the graph if need be
|
|
if draw:
|
|
draw_network(ctx, output_dir, G)
|
|
|
|
end = time.time()
|
|
time_took = end - start
|
|
print(f'For {G.number_of_nodes()}/{num_nodes} nodes, network generation took {time_took} secs.')
|
|
print(f'The generated network ({network_type.value}) is under ./{output_dir}')
|
|
|
|
# Benchmarking. Record finish time and stop the malloc tracing
|
|
if benchmark:
|
|
mem_curr, mem_max = tracemalloc.get_traced_memory()
|
|
tracemalloc.stop()
|
|
print(f"STATS: For {num_nodes} nodes, time took is {time_took} secs, peak memory usage is {mem_max/(1024*1024)} MBs\n")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
typer.run(main)
|
|
|
|
|