I want to know where does SimObject names like mem_ctrls, membus, replacement_policy are set in gem5. After looking at the code, I understood that, these name are used in stats.txt.
I have looked into SimObject code files(py,cc,hh files). I printed all Simobject names by stepping through root descendants in Simulation.py and then searched some of the names like mem_ctrls using vscode, but could not find a place where these names are set.
for obj in root.descendants():
print("object name:%s\n"% obj.get_name())
These names are the Python variable names from the configuration/run script.
For instance, from the Learning gem5 simple.py script...
from m5.objects import *
# create the system we are going to simulate
system = System()
# Set the clock fequency of the system (and all of its children)
system.clk_domain = SrcClockDomain()
system.clk_domain.clock = '1GHz'
system.clk_domain.voltage_domain = VoltageDomain()
# Set up the system
system.mem_mode = 'timing' # Use timing accesses
system.mem_ranges = [AddrRange('512MB')] # Create an address range
The names will be system, clk_domain, mem_ranges.
Note that only the SimObjects will have a name. The other parameters (e.g., integers, etc.) will not have a name.
You can see where this is set here: https://gem5.googlesource.com/public/gem5/+/master/src/python/m5/SimObject.py#1352
Related
I have a problem with terraform configuration which I really don't know how to resolve. I have written module for policy assigments, module as parameters taking object with 5 attributes. The question is if it is possible to split into folder structure tfvars file. I mean eg.
I have main folder subscriptions -> folder_subscription_name -> some number of files tfvars with configuration for each of the policy assigment
Example of each of the file
testmap = {
var1 = "test1"
var2 = "test2"
var3 = "test3"
var4 = "test4"
var5 = "test5"
}
In the module I would like to iterate over all of the maps combine into list of maps. Is it good approach? How to achive that or maybe I should use something other to do it like terragrunt ?
Please give me some tips what is the best way to achive that, basically the goal is that I don't want to have one insanely big tvars file with list of 100 maps but splitted into 100 configuration file for each of the assignment.
Based on the comment on the original question:
The question is preety simple how to keep input variables for each of
resource in seperate file instead of keeping all of them in one very
big file
I would say you aim at having different .tfvars files. For example, you could have a dev.tfvars and a prod.tfvars file.
To plan your deployment, you can pass those file with:
terraform plan --var-file=dev.tfvars -out planoutput
To apply those changes
terraform apply planoutput
How can I stop Splunk considering hostname "host" more important than "host" key?
Let's suppose that I have the following logs:
color = red ; host = localhost
color = blue ; host = newhost
The following query works fine:
index=myindex | stats count by color
but the following doesn't:
index=myindex | stats count by host
because instead of considering "host" being the key from the log, it sees the Host header as "host".
How can I deal with this?
When there are two fields with the same name one of them has to "win". In this case, it's the one Splunk defines before it processes the event itself. As you probably know, every event is given 4 fields at input time: index, host, source, and sourcetype. Data from the event won't override these unless specifically told to do so in the config files.
To override the settings, put this in your transforms.conf file
[sethost]
REGEX = host\s*=\s*(\w+)
DEST_KEY = MetaData:Host
FORMAT = host::$1
You'll also need to reference the transform in your props.conf file
[mysourcetype]
TRANSFORMS-host = sethost
I would have thought this solution would be more prominent, but I found it buried deep in the Splunk docs.
https://docs.splunk.com/Documentation/Splunk/8.2.6/Metrics/Search
You can use reserved fields such as "source", "sourcetype", or "host" as dimensions. However, when extracted dimension names are reserved names, the name is prefixed with "extracted_" to avoid name collision. For example, if a dimension name is "host", search for "extracted_host" to find it.
So, in your case:
index=myindex | stats count by extracted_host
I'm using tf.summary.histogram(var_name, var, family='my_family') to log a histogram. In the tensorboard interface it appears as
my_family/my_family/var_name
Does anybody know what the logic is behind duplicating the family name?
It does seem intentional, as I find the following in tensorflow/tensorflow/python/ops/summary_op_util.py :
# Use family name in the scope to ensure uniqueness of scope/tag.
scope_base_name = name if family is None else '{}/{}'.format(family, name)
with ops.name_scope(scope_base_name, default_name, values) as scope:
if family is None:
tag = scope.rstrip('/')
else:
# Prefix our scope with family again so it displays in the right tab.
tag = '{}/{}'.format(family, scope.rstrip('/'))
The first time family is inserted in scope_base_name = name if family is None else '{}/{}'.format(family, name), and the second time in tag = '{}/{}'.format(family, scope.rstrip('/')), which according to the comments in the code was deliberate.
I too was frustrated by this, but in the context of using tf.summary.scalar. I've resorted to using:
tf.summary.scalar('myfamily/myname', var)
Now the variables show up in Tensorboard without the duplication of the family name.
P.S. I would have made this a "comment" instead of an answer, but my reputation is too low.
I'm building a tree in Graphviz and I can't seem to be able to get the feature names to show up, I have defined a list with the feature names like so:
names = list(df.columns.values)
Which prints:
['Gender',
'SuperStrength',
'Mask',
'Cape',
'Tie',
'Bald',
'Pointy Ears',
'Smokes']
So the list is being created, later I build the tree like so:
export_graphviz(tree, out_file=ddata, filled=True, rounded=True, special_characters=False, impurity=False, feature_names=names)
But the final image still has the feature names listed like X[]:
How can I get the actual feature names to show up? (Cape instead of X[3], etc.)
I can only imagine this has to do with passing the names as an array of the values. It works fine if you pass the columns directly:
export_graphviz(tree, out_file=ddata, filled=True, rounded=True, special_characters=False, impurity=False, feature_names=df.columns)
If needed, you can also slice the columns:
export_graphviz(tree, out_file=ddata, filled=True, rounded=True, special_characters=False, impurity=False, feature_names=df.columns[5:])
Apologies if this is a straightforward question, I couldn't find anything in the docs.
currently my workflow looks something like this. I'm taking a number of input files created as part of this workflow, and summarizing them.
Is there a way to avoid this manual regex step to parse the wildcards in the filenames?
I thought about an "expand" of cross_ids and config["chromosomes"], but unsure to guarantee conistent order.
rule report:
output:
table="output/mendel_errors.txt"
input:
files=expand("output/{chrom}/{cross}.in", chrom=config["chromosomes"], cross=cross_ids)
params:
req="h_vmem=4G",
run:
df = pd.DataFrame(index=range(len(input.files), columns=["stat", "chrom", "cross"])
for i, fn in enumerate(input.files):
# open fn / make calculations etc // stat =
# manual regex of filename to get chrom cross // chrom, cross =
df.loc[i] = stat, chrom, choss
This seems a bit awkward when this information must be in the environment somewhere.
(via Johannes Köster on the google group)
To answer your question:
Expand uses functools.product from the standard library. Hence, you could write
from functools import product
product(config["chromosomes"], cross_ids)