gym | Testing Framework for Automated NFV Performance Benchmarking | Performance Testing library
kandi X-RAY | gym Summary
kandi X-RAY | gym Summary
This is a project to bring into reality the ideas designed in VBaaS (VNF Benchmarking-as-a-Service). The main purpose of this source code is a modular ad-hoc platform for testing VNFs and their respective infrastructure. Gym is a reference implementation of the ongoing draft in the Benchmarking Methodology Working Group (BMWG) in Internet Engineering Task Force (IETF), named Methodology for VNF Benchmarking Automation.
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Top functions reviewed by kandi - BETA
- Monitor the running process
- Get the statistics for the process
- Get process memory
- Get CPU statistics
- Listen for commands
- Return a list of items
- Serialize options into a dictionary
- Start a process
- Stop the process after timeout seconds
- Serialize the options dictionary
- Serialize options to a dictionary
- Process an input message
- Returns a dict with the stats of the server
- Return a list of outputs
- Convert options to a dictionary
- Serialize the options
- Compile vnf - pp
- Deal with peer info
- Generate the options for the given opts
- Handle incoming request
- Handle the result of a vnf - b
- Return a list of measurement statistics for the given metric
- Parse config
- Monitor node stats
- Run the process
- Parse a JSON response
- Load a VNF -BD file
- Execute stats on the machine
gym Key Features
gym Examples and Code Snippets
Lines of Code : 30
License : No License
Copydef main():
env = gym.make('CartPole-v0')
ft = FeatureTransformer(env)
model = Model(env, ft)
gamma = 0.99
if 'monitor' in sys.argv:
filename = os.path.basename(__file__).split('.')[0]
monitor_dir = './' + filename + '_' + str(date def __init__(self, env, k):
"""Stack k last frames.
Returns lazy array, which is much more memory efficient.
See Also
--------
baselines.common.atari_wrappers.LazyFrames
"""
gym.Wrapper.__init def __init__(self, env, rank=0):
gym.Wrapper.__init__(self, env=env)
self.rank = rank
self.rewards = []
self.total_reward = []
self.summaries_dict = {'reward': 0, 'episode_length': 0, 'total_reward': 0, 'total_ Community Discussions
Trending Discussions on gym
QUESTION
I read this answer, which clarified a lot of things, but I'm still confused about how I should go about designing my primary key.
First off I want to clarify the idea of WCUs. I get that WCU is the write capacity of max 1kb per second. Does it mean that if writing a piece of data takes 0.25 seconds, I would need 4 of those to be billed 1 WCU? Or each time I write something it consumes 1 WCU, but I could also write X times within 1 second and still be billed 1 WCU?
Usage
I want to create a table that stores the form data for a set of gyms (95% will be waivers, the rest will be incidents reports). Most of the time, each forms will be accessed directly via its unique ID. I also want to query the forms by date, form, userId, etc..
We can assume an average of 50k forms per gym
Options
First option is straight forward: having the formId be the partition key. What I don't like about this option is that scan operations will always filter out 90% of the data (i.e. the forms from other gyms), which isn't good for RCUs.
Second option is that I would make the gymId the partition key, and add a sort key for the date, formId, userId. To implement this option I would need to know more about the implications of having 50k records on one partition key.
Third option is to have one table per gyms and have the formId as partition key. This seems to be like the best option for now, but I don't really like the idea of having a a large number of tables doing the same thing in my account.
Is there another option? Which one of the three is better?
Edit: I'm assuming another option would be SimpleDB?
...ANSWER
Answered 2021-May-21 at 20:26For your PK design. What data does the app have when a user is going to look for a form? Does it have the GymID, userID, and formID? If so, make a compound key out of that for the PK perhaps? So your PK might look like:
QUESTION
First time asking a question on here, so I apologise if I have missed something. I have been looking through existing answers and couldn't find any that address this issue specifically.
I'm trying to split inconsistent strings into two variables using the extract function of the tidyr package.
Reprex of my data with library calls:
...ANSWER
Answered 2021-Jun-14 at 15:07You used lookarounds that are non-consuming patterns, while you need to use consuming pattern to let the regex engine reach minutes after hours.
You can solve the problem using
QUESTION
I am training a DDPG agent on my custom environment that I wrote using openai gym. I am getting error during training the model.
When I search for a solution on web, I found that some people who faced similar issue were able to resolve it by initializing the variable.
...ANSWER
Answered 2021-Jun-10 at 07:00For now I was able to solve this error by replacing the imports from keras with imports from tensorflow.keras, although I don't know why keras itseld doesn't work
QUESTION
I'm writing a check in app for my gym in XCode. I asked this a year and a half ago for Android: Is there a way around doing 2000 else if statements in this?
But now, I am reaching 2000 members and need to add more. Unfortunately, and obviously, Xcode is crashing when I try to add more lines.
My code is this:
...ANSWER
Answered 2021-Jun-08 at 23:59Here's a basic solution to split the string and use part of it in the image name:
QUESTION
Trying to figure out how I add a shadow to 3 images I have central on my page, looks a little weird without one I believe.
At the moment my HMTL code for this is below:
...ANSWER
Answered 2021-Jun-08 at 08:44You can do that by using a box-shadow generator for CSS3.
You need to copy the box-shadow and add it to the image.
QUESTION
I'm a beginner in python so I have this program where it classifies tweets into different categories (sport,sante, culture...) using keywords and I would like to copy-paste every line of the JSON file that belongs to a certain category into a file named text1 and I did the following : but I guess I did it the wrong way since I keep receiving the same error please any suggestion on how to solve this problem!
...ANSWER
Answered 2021-Jun-06 at 22:54This might be a very simple case of fixing the encoding.
Your error says:
QUESTION
I'm trying to solve the OpenAI gym Breakout-V0 with a Deep Q-Network Agent.
Every time when my agent reaches the point where:
- The replay_memory is filled enough to start training
- The copy_target_network interval is reached for the first time
- The target_network predicts for the fist time
Tensorflow throws following error:
...ANSWER
Answered 2021-Jun-04 at 08:39As Dr.Snoopy said, it's a simple solution
Just had to do np.reshape(state, (1, 33600))
QUESTION
So I am looking to train a model on colab using a GPU/TPU as my local machine doesn't have one. I am not bothered about visualising the training I just want colab to do the bulk of the work.
When importing my .ipynb into colab and running as soon as i attempt to make an env using any of the atari games i get the error:
...ANSWER
Answered 2021-Jun-03 at 12:26So I have found a solution. You will first need to download the roms from http://www.atarimania.com/rom_collection_archive_atari_2600_roms.html
Unpack the .rar file then unzip the HC Roms and Roms folders.
Next upload the folders to colab or to your Google Drive and then link it to your colab.
From here run:
QUESTION
I am implementing simple DQN algorithm using pytorch, to solve the CartPole environment from gym. I have been debugging for a while now, and I cant figure out why the model is not learning.
Observations:
- using
SmoothL1Lossperforms worse thanMSEloss, but loss increases for both - smaller
LRinAdamdoes not work, I have tested using 0.0001, 0.00025, 0.0005 and default
Notes:
- I have debugged various parts of the algorithm individually, and can say with good confidence that the issue is in the
learnfunction. I am wondering if this bug is due to me misunderstandingdetachin pytorch or some other framework mistake im making. - I am trying to stick as close to the original paper as possible (linked above)
References:
...ANSWER
Answered 2021-Jun-02 at 17:39The main problem I think is the discount factor, gamma. You are setting it to 1.0, which mean that you are giving the same weight to the future rewards as the current one. Usually in reinforcement learning we care more about the immediate reward than the future, so gamma should always be less than 1.
Just to give it a try I set gamma = 0.99 and run your code:
QUESTION
I am trying to stop the contents of the div ("check-group") from overflowing out of the the parent div("form-div") on smaller screens such as mobiles. But nothing works. Any help is much appreciated.What can I do to prevent overflowing? (That's the detail I can provide. sorry for bad english.)
Thanks in advance.
ANSWER
Answered 2021-Jun-02 at 10:51The main problem is 'white-space' propertiy which you have used. Please use the following css code below: (insted of your css code)
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No vulnerabilities reported
Install gym
You can use gym like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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