rce-agent | gRPC-based Remote Command Execution Agent | TLS library
kandi X-RAY | rce-agent Summary
kandi X-RAY | rce-agent Summary
rce-agent is a gRPC-based Remote Command Execution (RCE) client and server. The server (or "agent") runs on a remote host and executes a whitelist of shell commands specified in a file. The client calls the agent to execute whitelist commands. TLS is used to secure and authenticate both client and server. rce-agent replaces SSH and other methods of remote code execution. There are no passwords—only TLS certificates—and commands are limited to a whitelist. This eliminates the need for SSH keys, passwords, or forwarding. This package is meant to be integrated into your code. The rce.Client and rce.Server objects do all the heavy lifting so your client and agent code can focus on their domain-specific logic. See example/ for example code. RCE Agent is also meant to be used with your private certificate authority (CA) for TLS-encrypted communication and mutual authentication of client and agent. (Setting up a private CA is beyond the scope of this project, but we highly suggest you use one!) Normally, only the client verifies the server's TLS certificate (cert). For additional security, your code should use rce.TLSFiles to create Go tls.Config which makes the server (agent) verify the client's cert, too.
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Top functions reviewed by kandi - BETA
- Run the remote whitelist command .
- mapStatus maps command status to pb . Status
- NewServerWithConfig returns a new server instance .
- LoadCommands loads a list of commands .
- _RCEAgent_Start_Handler is called on RCE agent .
- _RCEAgent_Wait_Handler is a wrapper around the RCEAgentServer interface .
- _RCEAgent_GetStatus_Handler is an interceptor for RCE agent .
- _RCEAgent_Stop_Handler invokes the rCEAgentServer interface .
- NewCmd returns a new gocmd . Command .
- printOutput prints a line of lines .
rce-agent Key Features
rce-agent Examples and Code Snippets
Community Discussions
Trending Discussions on rce-agent
QUESTION
I am implementing REINFORCE applied to the CartPole-V0 openAI gym environment. I am trying 2 different implementations of the same, and the issue I am not able to resolve is the following:
Upon passing a single state to the Policy Network, I get an output Tensor of size 2, containing the action probabilities of the 2 actions. However, when I pass a `batch of states' to the Policy Network to compute the output action probabilities of all of them, the values that I obtain are very different from when each state is individually passed to the network.
Can someone help me understand the issue?
My code for the same is below: (Note: this is NOT the complete REINFORCE algorithm -- I am aware that I need to compute the loss from the probabilities. But I am trying to understand the difference in the computation of the two probabilities, which I think should be the same, before proceeding.)
...ANSWER
Answered 2021-Nov-27 at 08:21In your policy, you have Softmax over dim 0. This normalizes the probability of each action across your batch. You want to do it across actions by dim=1.
QUESTION
This is what I tried:
...ANSWER
Answered 2020-Feb-21 at 16:52You can check the path by going to Tools -> SDK Manager and looking at Android SDK Location. Generally, the adb is present under the platform-tools folder present under this Android SDK Location.
At that location you need to execute ./adb
.
Else you can also export that platform-tools path to access adb from anywhere, as follow:
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