andreaschandra

[Unit]
Description=MLFlow Server

[Service]
Type=simple

ExecStart=/bin/bash -c 'PATH=/home/<username>/miniconda3/bin/:$PATH exec mlflow server --backend-store-uri postgresql://mlflow:mlflow@localhost/mlflow --default-artifact-root file:/home/andreas/mlruns -h 0.0.0.0 -p 8000'

User=<user>
Group=<group>

andreaschandra

class ResNet(nn.Module):
    def __init__(
        self,
        block: Type[Union[BasicBlock, Bottleneck]],
        layers: List[int],
        num_classes: int = 1000,
        zero_init_residual: bool = False,
        groups: int = 1,
        width_per_group: int = 64,
        replace_stride_with_dilation: Optional[List[bool]] = None,

andreaschandra

class BasicBlock(nn.Module):
    expansion: int = 1

    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        groups: int = 1,

andreaschandra

def conv3x3(
    in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1
) -> nn.Conv2d:
    """3x3 convolution with padding"""
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=dilation,

andreaschandra

from typing import Type, Any, Callable, Union, List, Optional

import torch
from torch import nn
from torch import Tensor

andreaschandra

from datetime import datetime, timedelta
import airflow
from airflow import DAG
from custom import MySqlToPostgreOperator

dag = DAG(
    dag_id="a_job_near_rt",
    start_date=datetime.now() - timedelta(hours=1),
    schedule_interval="* * * * *",
    concurrency=100

andreaschandra

from datetime import datetime, timedelta
import airflow
from airflow import DAG
from custom import MySqlToPostgreOperator

dag = DAG(
    dag_id="job_trial_user",
    start_date=datetime.today() - timedelta(days=1),
    schedule_interval="0 */4 * * *",
    concurrency=100

andreaschandra

from airflow.hooks.base import BaseHook
from airflow.hooks.mysql_hook import MySqlHook
from airflow.providers.postgres.hooks.postgres import PostgresHook
from airflow.models.baseoperator import BaseOperator
from airflow.utils.decorators import apply_defaults


class MySqlToPostgreOperator(BaseOperator):
    
    @apply_defaults

andreaschandra

from datetime import datetime, timedelta
import airflow
from airflow import DAG
from airflow.operators.bash import BashOperator

dag = DAG(
    dag_id="get_instance_v6",
    start_date=datetime.today() - timedelta(minutes=60),
    schedule_interval="*/5 * * * *",
)

andreaschandra

# Training Routine
# set 100 epoch
for epoch in range(1, 101):
    train_loss = 0
    test_loss = 0
    
    model.train()
    train_gen = DataLoader(dataset_train, batch_size=2)
    for batch_index, (x, y) in enumerate(train_gen, 1):