Datasets:
jblitzar
/
github-python

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param_group['lr'] = learning_rate
val_loss_old = val_loss # Update old validation loss
if epoch % test_every == 0 or epoch == max_epochs-1:
# Test
test_time, test_loss, test_err_edges, test_err_tour, test_err_tsp, test_pred_tour_len, test_gt_tour_len = test(net, config, epoch_bar, mode='test')
epoch_bar.write('T: ' + metrics_to_str(epoch, test_time, learning_rate, test_loss, test_err_edges, test_err_tour, test_err_tsp, test_pred_tour_len, test_gt_tour_len))
writer.add_scalar('loss/test_loss', test_loss, epoch)
writer.add_scalar('pred_tour_len/test_pred_tour_len', test_pred_tour_len, epoch)
writer.add_scalar('optimality_gap/test_opt_gap', test_pred_tour_len/test_gt_tour_len - 1, epoch)
# Save training checkpoint at the end of epoch
torch.save({
'epoch': epoch,
'model_state_dict': net.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'train_loss': train_loss,
'val_loss': val_loss,
}, log_dir+"last_train_checkpoint.tar")
# Save checkpoint after every 250 epochs
if epoch != 0 and (epoch % 250 == 0 or epoch == max_epochs-1):
torch.save({
'epoch': epoch,
'model_state_dict': net.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'train_loss': train_loss,
'val_loss': val_loss,
}, log_dir+f"checkpoint_epoch{epoch}.tar")
return net
if __name__ == "__main__":
main(config)
# <FILESEP>
from enum import Enum
from fastapi import Request, FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
import os
import aiohttp
import json
from modal import Image, Mount, Secret, Stub, asgi_app
from utils import pretty_log
image = Image.debian_slim(python_version="3.10").pip_install("pynacl", "requests")
discord_secrets = [Secret.from_name("discord-secret-fsdl")]
stub = Stub(
"askfsdl-discord",
image=image,
secrets=discord_secrets,
mounts=[Mount.from_local_python_packages("utils")],
)
class DiscordInteractionType(Enum):
PING = 1 # hello from Discord
APPLICATION_COMMAND = 2 # an actual command
class DiscordResponseType(Enum):
PONG = 1 # hello back
DEFERRED_CHANNEL_MESSAGE_WITH_SOURCE = 5 # we'll send a message later
class DiscordApplicationCommandOptionType(Enum):
STRING = 3 # with language models, strings are all you need
@stub.function(
# keep one instance warm to reduce latency, consuming ~0.2 GB while idle
# this costs ~$3/month at current prices, so well within $10/month free tier credit
keep_warm=1,
)
@asgi_app(label="askfsdl-discord-bot")
def app() -> FastAPI:
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.post("/")
async def handle_request(request: Request):
"Verify incoming requests and if they're a valid command spawn a response."
# while loading the body, check that it's a valid request from Discord
body = await verify(request)
data = json.loads(body.decode())
if data.get("type") == DiscordInteractionType.PING.value: