I am stumped. It should be a non-issue.
I took a patch from Scala 2 repository for a bug which crashes the compiler, compiled those classes separately against "scala-lang" % "scala-compiler" % "2.13.10" and packed into a jar. Then I tried to put them on boot class path of SBT, so they would be picked over the class loader used by the compiler:
scalacOptions ++= Seq(
"-JXbootclasspath/a:/home/turin/scala/patches/ConcurrentModificationExceptionPatch.jar",
"-bootclasspath:/home/turin/scala/patches/ConcurrentModificationExceptionPatch.jar",
)
The options above didn't work, and neither did
javaOptions ++= Seq(
"-Xbootclasspath/a:/home/turin/scala/patches/ConcurrentModificationExceptionPatch.jar",
)
I know the compiler does not see them, because line numbers in the stack trace of the exception thrown during compilation match release files, not 'my' files.
SBT recognizes the Scalac bootclasspath, but, apparently, not the java one. Here's the output generated with "-Ylog-classpath":
scalac: Classpath built from (-bootclasspath = /home/turin/scala/patches/ConcurrentModificationExceptionPatch.jar -encoding = UTF-8 -feature = true -g = vars -verbose = true -Wconf = List(msg=export:silent, cat=other-match-analysis&msg=Singleton():silent, cat=deprecation&msg=foldLeft:silent, cat=deprecation&msg=foldRight:silent, cat=deprecation:w, cat=feature:w, cat=feature:w, cat=deprecation:ws, cat=feature:ws, cat=optimizer:ws) -Wperformance = PerformanceWarnings.ValueSet() -Wunused = UnusedWarnings.ValueSet(patvars, privates, locals) -Xlint = LintWarnings.ValueSet(nullary-unit, poly-implicit-overload, option-implicit, delayedinit-select, implicit-not-found) -classpath = /home/turin/porn/oldsql/target/scala-2.13/classes:/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/io/github/java-diff-utils/java-diff-utils/4.12/java-diff-utils-4.12.jar:/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/net/bytebuddy/byte-buddy/1.12.23/byte-buddy-1.12.23.jar:/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/net/java/dev/jna/jna/5.9.0/jna-5.9.0.jar:/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/jline/jline/3.21.0/jline-3.21.0.jar:/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/scala-lang/scala-compiler/2.13.10/scala-compiler-2.13.10.jar:/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/scala-lang/scala-library/2.13.10/scala-library-2.13.10.jar:/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/scala-lang/scala-reflect/2.13.10/scala-reflect-2.13.10.jar -language = languageFeatures.ValueSet(existentials, higherKinds, implicitConversions, reflectiveCalls) -Vlog = List(0-100) -Vclasspath = true -Vprint-types = true -Vimplicits = true -Vimplicits-verbose-tree = true -Vreflective-calls = true)
Sorry for the mile long line, I didn't want to edit it for fear of removing something important.
scalac: Defaults: object Defaults {
scalaHome =
javaBootClassPath =
scalaLibDirFound = None
scalaLibFound =
scalaBootClassPath =
scalaPluginPath = misc/scala-devel/plugins
}
calac: Calculated: object Calculated {
scalaHome =
javaBootClassPath =
javaExtDirs =
javaUserClassPath =
useJavaClassPath = false
scalaBootClassPath = /home/turin/scala/patches/ConcurrentModificationExceptionPatch.jar
scalaExtDirs =
userClassPath =
/home/turin/porn/oldsql/target/scala-2.13/classes
/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/io/github/java-diff-utils/java-diff-utils/4.12/java-diff-utils-4.12.jar
/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/net/bytebuddy/byte-buddy/1.12.23/byte-buddy-1.12.23.jar
/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/net/java/dev/jna/jna/5.9.0/jna-5.9.0.jar
/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/jline/jline/3.21.0/jline-3.21.0.jar
/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/scala-lang/scala-compiler/2.13.10/scala-compiler-2.13.10.jar
/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/scala-lang/scala-library/2.13.10/scala-library-2.13.10.jar
/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/scala-lang/scala-reflect/2.13.10/scala-reflect-2.13.10.jar
sourcePath =
}
scalac: After java boot/extdirs classpath has 9 entries:
ZipArchiveClassPath(/home/turin/scala/patches/ConcurrentModificationExceptionPatch.jar,None)
DirectoryClassPath(/home/turin/porn/oldsql/target/scala-2.13/classes)
ZipArchiveClassPath(/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/io/github/java-diff-utils/java-diff-utils/4.12/java-diff-utils-4.12.jar,None)
ZipArchiveClassPath(/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/net/bytebuddy/byte-buddy/1.12.23/byte-buddy-1.12.23.jar,None)
ZipArchiveClassPath(/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/net/java/dev/jna/jna/5.9.0/jna-5.9.0.jar,None)
ZipArchiveClassPath(/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/jline/jline/3.21.0/jline-3.21.0.jar,None)
ZipArchiveClassPath(/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/scala-lang/scala-compiler/2.13.10/scala-compiler-2.13.10.jar,None)
ZipArchiveClassPath(/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/scala-lang/scala-library/2.13.10/scala-library-2.13.10.jar,None)
ZipArchiveClassPath(/home/turin/.cache/coursier/v1/https/repo1.maven.org/maven2/org/scala-lang/scala-reflect/2.13.10/scala-reflect-2.13.10.jar,None)
What am I doing wrong?
Related
Currently I'm tryin to convert given onnx file to tensorrt file, and do inference on the generated tensorrt file.
To do so, I used tensorrt python binding API, but
"Error Code 1: Cuda Driver (invalid resource handle)" happens and there is no kind description about this.
Can anyone help me to overcome this situation?
Thx in advance, and below is my code snippet.
def trt_export(self):
fp_16_mode = True
## Obviously, I provided appropriate file names
trt_file_name = "PATH_TO_TRT_FILE"
onnx_name = "PATH_TO_ONNX_FILE"
TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE)
EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network(EXPLICIT_BATCH)
parser = trt.OnnxParser(network, TRT_LOGGER)
config = builder.create_builder_config()
config.max_workspace_size = (1<<30)
config.set_flag(trt.BuilderFlag.FP16)
config.default_device_type = trt.DeviceType.GPU
profile = builder.create_optimization_profile()
profile.set_shape('input', (1, 3, IMG_SIZE, IMG_SIZE), (12, 3, IMG_SIZE, IMG_SIZE), (32, 3, IMG_SIZE, IMG_SIZE)) # random nubmers for min. opt. max batch
config.add_optimization_profile(profile)
with open(onnx_name, 'rb') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
engine = builder.build_engine(network, config)
buf = engine.serialize()
with open(trt_file_name, 'wb') as f:
f.write(buf)
def validate_trt_result(self, input_path):
TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE)
trt_file_name = "PATH_TO_TRT_FILE"
trt_runtime = trt.Runtime(TRT_LOGGER)
with open(trt_file_name, 'rb') as f:
engine_data = f.read()
engine = trt_runtime.deserialize_cuda_engine(engine_data)
cuda.init()
device = cuda.Device(0)
ctx = device.make_context()
inputs, outputs, bindings = [], [], []
context = engine.create_execution_context()
stream = cuda.Stream()
index = 0
for binding in engine:
size = trt.volume(engine.get_binding_shape(binding)) * -1 # assuming one batch
dtype = trt.nptype(engine.get_binding_dtype(binding))
host_mem = cuda.pagelocked_empty(size, dtype)
device_mem = cuda.mem_alloc(host_mem.nbytes)
bindings.append(int(device_mem))
if engine.binding_is_input(binding):
inputs.append(HostDeviceMem(host_mem, device_mem))
context.set_binding_shape(index, [1, 3, IMG_SIZE, IMG_SIZE])
else:
outputs.append(HostDeviceMem(host_mem, device_mem))
index += 1
print(context.all_binding_shapes_specified)
input_img = cv2.imread(input_path)
input_r = cv2.resize(input_img, dsize = (256, 256))
input_p = np.transpose(input_r, (2, 0, 1))
input_e = np.expand_dims(input_p, axis = 0)
input_f = input_e.astype(np.float32)
input_f /= 255
numpy_array_input = [input_f]
hosts = [input.host for input in inputs]
trt_types = [trt.int32]
for numpy_array, host, trt_types in zip(numpy_array_input, hosts, trt_types):
numpy_array = np.asarray(numpy_array).astype(trt.nptype(trt_types)).ravel()
print(numpy_array.shape)
np.copyto(host, numpy_array)
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
#### ERROR HAPPENS HERE ####
context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
#### ERROR HAPPENS HERE ####
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
stream.synchronize()
print("TRT model inference result : ")
output = outputs[0].host
for one in output :
print(one)
ctx.pop()
Looks like ctx.push() line is missing before a line with memcpy_htod_async.
Such a error can happen if TensorFlow / PyTorch is also using CUDA in parallel with TensorRT.
See the related question/answer: https://stackoverflow.com/a/73996477/5655977
I want to deploy influxDB with collectd.
For previous version ie 1.8 of influxDB it was supported.
Do we have support for collectd in the latest version of influxdb i.e 2.0?
This functionality was moved to telegraf. in /etc/telegraf/telegraf.conf you have to define a collectd input and an influxdb output, then it works. I haven't found an example so I had to piece it together from different sources of documentation:
[agent]
interval = "10s"
round_interval = true
metric_batch_size = 1000
metric_buffer_limit = 10000
collection_jitter = "0s"
flush_interval = "10s"
flush_jitter = "0s"
precision = ""
hostname = ""
omit_hostname = false
[[inputs.socket_listener]]
service_address = "udp://:25826"
data_format = "collectd"
collectd_auth_file = "/etc/collectd/collectd.auth"
collectd_security_level = "encrypt"
collectd_typesdb = ["/usr/share/collectd_types.db"]
collectd_parse_multivalue = "split"
[[outputs.influxdb_v2]]
urls = ["http://influxdb:8086"]
token = "$DOCKER_INFLUXDB_INIT_ADMIN_TOKEN"
organization = "$DOCKER_INFLUXDB_INIT_ORG"
bucket = "$DOCKER_INFLUXDB_INIT_BUCKET"
First of all, I am using TFX version 0.21.2 and Tensorflow version 2.1.
I have constructed a pipeline largely following the Chigaco taxi example. When the Trainer component is executed I can see the following in the logs:
INFO - Training complete. Model written to /root/airflow/tfx/pipelines/fish/Trainer/model/9/serving_model_dir
When checking the above directory it is empty. What am I missing?
This is my DAG definition file (import statements omitted):
_pipeline_name = 'fish'
_airflow_config = AirflowPipelineConfig(airflow_dag_config = {
'schedule_interval': None,
'start_date': datetime.datetime(2019, 1, 1),
})
_project_root = os.path.join(os.environ['HOME'], 'airflow')
_data_root = os.path.join(_project_root, 'data', 'fish_data')
_module_file = os.path.join(_project_root, 'dags', 'fishUtils.py')
_serving_model_dir = os.path.join(_project_root, 'serving_model', _pipeline_name)
_tfx_root = os.path.join(_project_root, 'tfx')
_pipeline_root = os.path.join(_tfx_root, 'pipelines', _pipeline_name)
_metadata_path = os.path.join(_tfx_root, 'metadata', _pipeline_name,
'metadata.db')
def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
module_file: Text, serving_model_dir: Text,
metadata_path: Text,
direct_num_workers: int) -> pipeline.Pipeline:
examples = external_input(data_root)
example_gen = CsvExampleGen(input=examples)
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
infer_schema = SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
validate_stats = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=infer_schema.outputs['schema'])
trainer = Trainer(
examples=example_gen.outputs['examples'], schema=infer_schema.outputs['schema'],
module_file=_module_file, train_args= trainer_pb2.TrainArgs(num_steps=10000),
eval_args= trainer_pb2.EvalArgs(num_steps=5000))
model_validator = ModelValidator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'])
pusher = Pusher(
model=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
return pipeline.Pipeline(
pipeline_name=_pipeline_name,
pipeline_root=_pipeline_root,
components=[
example_gen,
statistics_gen,
infer_schema,
validate_stats,
trainer,
model_validator,
pusher],
enable_cache=True,
metadata_connection_config=metadata.sqlite_metadata_connection_config(
metadata_path),
beam_pipeline_args=['--direct_num_workers=%d' % direct_num_workers]
)
runner = AirflowDagRunner(config = _airflow_config)
DAG = runner.run(
_create_pipeline(
pipeline_name=_pipeline_name,
pipeline_root=_pipeline_root,
data_root=_data_root,
module_file=_module_file,
serving_model_dir=_serving_model_dir,
metadata_path=_metadata_path,
# 0 means auto-detect based on on the number of CPUs available during
# execution time.
direct_num_workers=0))
And this is my module file:
_DENSE_FLOAT_FEATURE_KEYS = ['length']
real_valued_columns = [tf.feature_column.numeric_column('length')]
def _eval_input_receiver_fn():
serialized_tf_example = tf.compat.v1.placeholder(
dtype=tf.string, shape=[None], name='input_example_tensor')
features = tf.io.parse_example(
serialized=serialized_tf_example,
features={
'length': tf.io.FixedLenFeature([], tf.float32),
'label': tf.io.FixedLenFeature([], tf.int64),
})
receiver_tensors = {'examples': serialized_tf_example}
return tfma.export.EvalInputReceiver(
features={'length' : features['length']},
receiver_tensors=receiver_tensors,
labels= features['label'],
)
def parser(serialized_example):
features = tf.io.parse_single_example(
serialized_example,
features={
'length': tf.io.FixedLenFeature([], tf.float32),
'label': tf.io.FixedLenFeature([], tf.int64),
})
return ({'length' : features['length']}, features['label'])
def _input_fn(filenames):
# TFRecordDataset doesn't directly accept paths with wildcards
filenames = tf.data.Dataset.list_files(filenames)
dataset = tf.data.TFRecordDataset(filenames, 'GZIP')
dataset = dataset.map(parser)
dataset = dataset.shuffle(2000)
dataset = dataset.batch(40)
dataset = dataset.repeat(10)
return dataset
def trainer_fn(trainer_fn_args, schema):
estimator = tf.estimator.LinearClassifier(feature_columns=real_valued_columns)
train_input_fn = lambda: _input_fn(trainer_fn_args.train_files)
train_spec = tf.estimator.TrainSpec(
train_input_fn,
max_steps=trainer_fn_args.train_steps)
eval_input_fn = lambda: _input_fn(trainer_fn_args.eval_files)
eval_spec = tf.estimator.EvalSpec(
eval_input_fn,
steps=trainer_fn_args.eval_steps,
name='fish-eval')
receiver_fn = lambda: _eval_input_receiver_fn()
return {
'estimator': estimator,
'train_spec': train_spec,
'eval_spec': eval_spec,
'eval_input_receiver_fn': receiver_fn
}
Thank you in advance for your help!
Posting the solution for anyone that is facing the same problem that I faced.
The reason that the model was not written in the filesystem was that the estimator needs a config argument to know where to write the model.
The following modification to the trainer_fn function should solve the problem.
run_config = tf.estimator.RunConfig(save_checkpoints_steps=999, keep_checkpoint_max=1)
run_config = run_config.replace(model_dir=trainer_fn_args.serving_model_dir)
estimator=tf.estimator.LinearClassifier(feature_columns=real_valued_columns,config=run_config)
I am trying to use to sen2r() function (Package sen2r_1.3.2) with default parameters but getting the following error:
Error in paste(c(...), collapse = sep) : argument is missing, with no default.
I know the error wants me to fill in some parameters, but the source manual clearly says that the default should work, and the parameters can be set subsequently upon launching the GUI.
Using the s2_gui() launches the shiny app, but keeps hanging when I try to "Save and Close"
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 LTS
Also, can someone with a 'higher reputation' please create a sen2r tag, for easier subsequent communications?
Here is the traceback...
sen2r()
Error in paste(c(...), collapse = sep) :
argument is missing, with no default
> traceback()
7: paste(c(...), collapse = sep)
6: strsplit(paste(c(...), collapse = sep), "\n")
5: unlist(strsplit(paste(c(...), collapse = sep), "\n"))
4: strwrap(unlist(strsplit(paste(c(...), collapse = sep), "\n")),
width = width, indent = indent, exdent = exdent, prefix = prefix,
initial = initial)
3: print_message(type = "waiting", "It seems you are running this package for the first time. ",
"Do you want to verify/install the required dependencies using a GUI (otherwise, an\n automatic check will be performed)? (y/n) ",
)
2: .sen2r(param_list = param_list, pm_arg_passed = pm_arg_passed,
gui = gui, preprocess = preprocess, s2_levels = s2_levels,
sel_sensor = sel_sensor, online = online, order_lta = order_lta,
apihub = apihub, downloader = downloader, overwrite_safe = overwrite_safe,
rm_safe = rm_safe, step_atmcorr = step_atmcorr, sen2cor_use_dem = sen2cor_use_dem,
sen2cor_gipp = sen2cor_gipp, max_cloud_safe = max_cloud_safe,
timewindow = timewindow, timeperiod = timeperiod, extent = extent,
extent_name = extent_name, s2tiles_selected = s2tiles_selected,
s2orbits_selected = s2orbits_selected, list_prods = list_prods,
list_rgb = list_rgb, list_indices = list_indices, index_source = index_source,
rgb_ranges = rgb_ranges, mask_type = mask_type, max_mask = max_mask,
mask_smooth = mask_smooth, mask_buffer = mask_buffer, clip_on_extent = clip_on_extent,
extent_as_mask = extent_as_mask, reference_path = reference_path,
res = res, res_s2 = res_s2, unit = unit, proj = proj, resampling = resampling,
resampling_scl = resampling_scl, outformat = outformat, rgb_outformat = rgb_outformat,
index_datatype = index_datatype, compression = compression,
rgb_compression = rgb_compression, overwrite = overwrite,
path_l1c = path_l1c, path_l2a = path_l2a, path_tiles = path_tiles,
path_merged = path_merged, path_out = path_out, path_rgb = path_rgb,
path_indices = path_indices, path_subdirs = path_subdirs,
thumbnails = thumbnails, parallel = parallel, processing_order = processing_order,
use_python = use_python, tmpdir = tmpdir, rmtmp = rmtmp,
log = log, globenv = sen2r_env, .only_list_names = FALSE)
1: sen2r()
I ran s2_gui() as is...no parameters specified. But i am running the dependency check now, I suspect that should clear things up even for the GUI.
This error was due to a code bug, which was fixed (see the GitHub issue 292).
Until the sen2r CRAN version will be updated, the bug can be:
solved installling the sen2r GitHub version (remotes::install_github("ranghetti/sen2r")), or
bypassed launching check_gdal() before running sen2r().
This is a bug in the original code.
In the traceback that you provided, it included:
3: print_message(type = "waiting", "It seems you are running this package for the first time. ",
"Do you want to verify/install the required dependencies using a GUI (otherwise, an\n automatic check will be performed)? (y/n) ",
)
Notably, I'll truncate most of the strings:
3: print_message(type = "waiting", "It seems ... time. ",
"Do you ... performed)? (y/n) ",
) # ^-- extra comma, invalid R syntax
Notice how it ends with a comma and then a right-paren? Yup, that's a syntax error in R. (This has been submitted as issue 292 on the original repo.)
The problem I am faced with regards to llvmlite is producing a simple hello world example.
I am unable to display the string global variable value in the function I created.
It appears to always print out the number one.
I have already tried to return the stringtype which produced a error.
from llvmlite import ir
i64 = ir.IntType(64)
i8 = ir.IntType(16)
hellostr = 'hello, world!'
stringtype = ir.ArrayType(i64, len(hellostr))
module = ir.Module( name="m_hello_example" )
hello = ir.GlobalVariable(module, stringtype, '.str4')
fn_int_to_int_type = ir.FunctionType(i64, [stringtype.as_pointer()] )
fn_hel = ir.Function( module, fn_int_to_int_type, name="fn_hel" )
fn_hel_block = fn_hel.append_basic_block( name="fn_hel_entry" )
builder = ir.IRBuilder(fn_hel_block )
# zero = builder.constant(i64, 0)
# const_1 = ir.Constant(stringtype,1);
# builder.ret(const_1)
const_1 = ir.Constant(i64,1);
# print(const_1)
builder.ret(const_1)
print( module )
I was expecting the output to print out the string 'hello, world!'.
Any help would be much appreciated.
Thanks.
It ended up that I was able to solve my problem with the following code:
import llvmlite.ir as ir
import llvmlite.binding as llvm
from ctypes import CFUNCTYPE
def main():
m = ir.Module()
func_ty = ir.FunctionType(ir.VoidType(), []) #defining printer function as type void
func = ir.Function(m, func_ty, name="printer") #define function as printer
builder = ir.IRBuilder(func.append_basic_block('entry')) #defining the entry point of the function printer
fmt = "%s\n\0" #in function printf allows for inserting arg in, next global_fmt statements allow for creating #"fstr" assignment
c_fmt = ir.Constant(ir.ArrayType(ir.IntType(8), len(fmt)),
bytearray(fmt.encode("utf8")))
global_fmt = ir.GlobalVariable(m, c_fmt.type, name="fstr")
global_fmt.linkage = 'internal'
global_fmt.global_constant = True
global_fmt.initializer = c_fmt
arg = "Hello, World!\0" #args will be passed into printf function.
c_str_val = ir.Constant(ir.ArrayType(ir.IntType(8), len(arg)),
bytearray(arg.encode("utf8"))) #creates the c_str_value as a constant
printf_ty = ir.FunctionType(ir.IntType(32), [], var_arg=True) #creation of the printf function begins here and specifies the passing of a argument
printf = ir.Function(m, printf_ty, name="printf")
c_str = builder.alloca(c_str_val.type) #creation of the allocation of the %".2" variable
builder.store(c_str_val, c_str) #store as defined on the next line below %".2"
voidptr_ty = ir.IntType(8).as_pointer()
fmt_arg = builder.bitcast(global_fmt, voidptr_ty) #creates the %".4" variable with the point pointing to the fstr
builder.call(printf, [fmt_arg, c_str]) #We are calling the prinf function with the fmt and arg and returning the value as defiend on the next line
builder.ret_void()
#Next lines are for calling llvm and returning the assembly.
llvm.initialize()
llvm.initialize_native_target()
llvm.initialize_native_asmprinter()
print(str(m)) #PRINTING OUT THE ASSEMBLY
llvm_module = llvm.parse_assembly(str(m)) #Parsing teh assembly
tm = llvm.Target.from_default_triple().create_target_machine() #creating the target machine
with llvm.create_mcjit_compiler(llvm_module, tm) as ee:
ee.finalize_object() #Making sure all modules owned by the execution engine are fully processed and usable for execution
fptr = ee.get_function_address("printer") #fptr will reference the printer function
py_func = CFUNCTYPE(None)(fptr)
py_func() #run the function printer
if __name__ == "__main__":
main()
It appears that I didn't correctly assign the variable and hence why I wasn't returning anything.