dsuess

import torch
from torch import nn
import tensorrt as trt

TRT_LOGGER = trt.Logger(trt.Logger.INFO)

class Model(nn.Module):

    def forward(self, x):
        y = (2 * x)[0:1]

dsuess

import numpy as np
import torch
from torch import nn
import onnx_tensorrt.backend as backend
import onnx

class Model(nn.Module):

    def forward(self, x):
        y = (2 * x)[0:1]

dsuess

"""
Python is a dynamic language, and it is relatively easy to dynamically create
and modify things such as classes and objects. Functions, however, are quite
challenging to create dynamically.
One area where we might want to do this is in an RPC library, where a function
defined on a server needs to be available remotely on a client.
The naive solution is to simply pass arguments to a generic function that
accepts `*args` and `**kwargs`. A lot of information is lost with this approach,
however, in particular the number of arguments taken. Used in an RPC
implementation, this also delays any error feedback until after the arguments

dsuess

source /home/ec2-user/anaconda3/bin/activate /home/ec2-user/anaconda3/envs/JupyterSystemEnv
jupyter labextension install jupyterlab_vim 
pip install jupyter_contrib_nbextensions
sudo -H -u ec2-user bash -c "source /home/ec2-user/anaconda3/bin/activate /home/ec2-user/anaconda3/envs/JupyterSystemEnv && jupyter-contrib nbextension install --user && jupyter-nbextension enable select_keymap/main"