# Based on https://discuss.pytorch.org/t/check-gradient-flow-in-network/15063/10 | |
def plot_grad_flow(named_parameters): | |
'''Plots the gradients flowing through different layers in the net during training. | |
Can be used for checking for possible gradient vanishing / exploding problems. | |
Usage: Plug this function in Trainer class after loss.backwards() as | |
"plot_grad_flow(self.model.named_parameters())" to visualize the gradient flow''' | |
ave_grads = [] | |
max_grads= [] |
version: "3" | |
networks: | |
kong-net: | |
driver: bridge | |
services: | |
####################################### | |
# Postgres: The database used by Kong |
/* | |
* ESP8266 Web server with Web Socket to control an LED. | |
* | |
* The web server keeps all clients' LED status up to date and any client may | |
* turn the LED on or off. | |
* | |
* For example, clientA connects and turns the LED on. This changes the word | |
* "LED" on the web page to the color red. When clientB connects, the word | |
* "LED" will be red since the server knows the LED is on. When clientB turns | |
* the LED off, the word LED changes color to black on clientA and clientB web |
-
Delete all containers
$ docker ps -q -a | xargs docker rm
-q prints only the container IDs -a prints all containers
Notice that it uses xargs to issue a remove container command for each container ID
- Delete all untagged images
So yeah... no documentation for the HBase REST API in regards to what should a filter look like...
So I installed Eclipse, got the library, and took some time to find some of the (seemingly) most useful filters you could use. I'm very green at anything regarding HBase, and I hope this will help anyone trying to get started with it.
What I discovered is that basically, attributes of the filter object follow the same naming than in the documentation. For this reason, I have made the link clickable and direct them to the HBase Class documentation attached to it; check for the instantiation argument names, and you will have your attribute list (more or less).