Timestepping stencil only runs in serial

I may be misunderstanding the combination of the parallel jit option with numba stencils, but so far I have not been able to parallelize any stencils successfully. I can run numba in parallel, though. My code returns the expected values of a finite difference time-stepping stencil for the isotropic model of seismic wave propagation. But it only runs in serial. Basically what I would like to calculate is:
next_array = 2*curr_array - prev_array + stencil(curr_array).
At the very least, I'd like to parallelize the "stencil" operation.
I'm running with the following versions
Ubuntu 16.04 LTS
numba==0.36.0.dev0+431.gb1275ced8.dirty
llvmlite==0.21.0.dev0+22.g4c67d65
Here is the full program I'm running, which works (i.e. returns the right values), but will only perform in serial. The stencil I'm trying to parallelize is titled "laplace"
from numba import stencil, jit
from numba import njit, prange
import numba
import numpy as np
from matplotlib import pyplot

#setting constants
vel = 3000.0
f0 = 15
nt = 50
dt = .002
nx = 50
ny = 50
nz = 50
h = vel/(f0*5.0)
x_idx = nx//2
y_idx = ny//2
z_idx = nz//2
radius = 8
coeff = [-3.0548446, 1.777777, -3.1111111/10, 7.572087/100, -1.767676/100, 3.480962/1000, -5.180005/10000, 5.074287/(10*10*10*10*10), -2.42812/np.power(10, 6)]

#stencil runs in serial
@stencil(neighborhood = ((-radius, radius), (-radius, radius), (-radius, radius),), standard_indexing=("coeff",) )
#@jit(nopython=True, parallel=True)
@numba.njit(parallel=True)
def laplace(a, coeff):
        laplace = coeff[0]*a[0, 0, 0]*3
        for i in range(1, radius+1):
                laplace+= coeff[i]*((a[0, 0, -i] + a[0, 0, i]) + (a[0, -i, 0] + a[0, i, 0]) + (a[-i, 0, 0] + a[i, 0, 0]))
        return laplace

#source injection
def Ricker(t, f0):
        r  = (np.pi*f0 * (t-1./f0))
        return (1-2.*r**2)*np.exp(-r**2)

source = []
for i in range(nt):
        source.append(Ricker(dt*i, f0))

#setting up arrays
prev_timestep = np.zeros((nx+2*radius)*(ny+2*radius)*(nz+2*radius)).reshape((nx+2*radius, ny+2*radius, nz+2*radius))
curr_timestep = np.zeros((nx+2*radius)*(ny+2*radius)*(nz+2*radius)).reshape((nx+2*radius, ny+2*radius, nz+2*radius))
next_timestep = np.zeros((nx+2*radius)*(ny+2*radius)*(nz+2*radius)).reshape((nx+2*radius, ny+2*radius, nz+2*radius))
curr_timestep[x_idx+radius, y_idx+radius, z_idx+radius] += source[0]*(vel*dt)**2

#time_stepping scheme. each timestep should be calculated in parallel
def time_step(next_timestep, curr_timestep, prev_timestep, nt):
        for i in range(1, nt):
                next_timestep = 2*curr_timestep - prev_timestep + laplace(curr_timestep, coeff)*((dt*vel/h)**2)
                next_timestep[x_idx+radius, y_idx+radius, z_idx+radius] += source[i]*(vel*dt)**2
                prev_timestep = curr_timestep
                curr_timestep = next_timestep
                print(next_timestep[x_idx+radius, y_idx+radius, z_idx+radius])

        return next_timestep

#final result and plot
next_timestep = time_step(next_timestep, curr_timestep, prev_timestep, nt)

fig, ax = pyplot.subplots()
next_timestep = (next_timestep[:, :, z_idx + radius])
cax = ax.imshow(next_timestep, interpolation='nearest', cmap='hot')
ax.set_title('Heatmap of pressure')
# Add colorbar, make sure to specify tick locations to match desired ticklabels
min, max = np.amin(next_timestep), np.amax(next_timestep)
print ("min, max of pressure array are: %f, %f" %(min, max))
cbar = fig.colorbar(cax, ticks=[min,(max+min)/2.0, max])
cbar.ax.set_yticklabels(['%.2f' %min, '%.2f' %((max+min)/2.0) ,'%.2f' %max])
pyplot.savefig('run_pressure.png')

Wow! 3D time stepping stencil!
Looks like what I've been trying to do on my side.
I wonder @ninegua, @DrTodd13 or @seibert is it possible to make this code work in parallel.
P.S. IMHO, examples like this are what missing in the original Numba.Stencil documentation...

It looks like you have laplace with both @stencil and @njit.  The laplace function should have the @stencil but the @njit should be moved to the time_step function.

So I've tried adding this line above time_step and received an error. I assumed parallelizing above "time_step" might cause the code to try to parallelize over time and space. For clarity, this is not my intent and would return the wrong result. I only want to parallelize over space. In other words, I want every line within "for i in range(1, nt)" to be completed before proceeding to the next line or exiting the loop. Maybe some kind of time-parallel behavior could be causing my error?
Anyway, I added this line above time_step and removed it from laplace:
@numba.njit(parallel=True)
Here's the error code:

/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/dataflow.py:379: RuntimeWarning: Python2 style print partially supported.  Please use Python3 style print.
"Python3 style print.", RuntimeWarning)
Traceback (most recent call last):
File "stencil_serial_laplace.py", line 73, in
next_timestep = time_step(next_timestep, curr_timestep, prev_timestep, nt)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/dispatcher.py", line 307, in _compile_for_args
return self.compile(tuple(argtypes))
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/dispatcher.py", line 579, in compile
cres = self._compiler.compile(args, return_type)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/dispatcher.py", line 80, in compile
flags=flags, locals=self.locals)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 766, in compile_extra
return pipeline.compile_extra(func)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 362, in compile_extra
return self._compile_bytecode()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 725, in _compile_bytecode
return self._compile_core()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 712, in _compile_core
res = pm.run(self.status)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 248, in run
raise patched_exception
numba.errors.LoweringError: Caused By:
Traceback (most recent call last):
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 240, in run
stage()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 647, in stage_nopython_backend
self._backend(lowerfn, objectmode=False)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 602, in _backend
lowered = lowerfn()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 589, in backend_nopython_mode
self.flags)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 885, in native_lowering_stage
lower.lower()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 135, in lower
self.lower_normal_function(self.fndesc)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 176, in lower_normal_function
entry_block_tail = self.lower_function_body()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 201, in lower_function_body
self.lower_block(block)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 216, in lower_block
self.lower_inst(inst)
File "/home/jrt54/anaconda2/lib/python2.7/contextlib.py", line 35, in exit
self.gen.throw(type, value, traceback)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/errors.py", line 265, in new_error_context
six.reraise(type(newerr), newerr, sys.exc_info()[2])
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/errors.py", line 259, in new_error_context
yield
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 216, in lower_block
self.lower_inst(inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 365, in lower_inst
func(self, inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/npyufunc/parfor.py", line 67, in _lower_parfor_parallel
lowerer, parfor, typemap, typingctx, targetctx, flags, {})
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/npyufunc/parfor.py", line 602, in _create_gufunc_for_parfor_body
locals)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 780, in compile_ir
lifted_from=lifted_from)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 370, in compile_ir
return self._compile_ir()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 732, in _compile_ir
return self._compile_core()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 712, in _compile_core
res = pm.run(self.status)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 248, in run
raise patched_exception
LoweringError: Caused By:
Traceback (most recent call last):
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 240, in run
stage()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 454, in stage_nopython_frontend
self.locals)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 867, in type_inference_stage
infer.build_constraint()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 800, in build_constraint
self.constrain_statement(inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 959, in constrain_statement
self.typeof_assign(inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 1021, in typeof_assign
self.typeof_global(inst, inst.target, value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 1147, in typeof_global
literal_value=gvar.value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 866, in lock_type
tv.lock(tp, loc=loc, literal_value=literal_value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 72, in lock
assert not self.locked
AssertionError
Failed at nopython (nopython frontend)
File "stencil_serial_laplace.py", line 63
[1] During: lowering "[LoopNest(index_variable = $parfor_index_var.79, range = (8, last_ind.114, 1)), LoopNest(index_variable = $parfor_index_var.80, range = (8, last_ind.117, 1)), LoopNest(index_variable = $parfor_index_var.81, range = (8, last_ind.120, 1))]{165: <ir.Block at stencil_serial_laplace.py (32)>, 207: <ir.Block at stencil_serial_laplace.py (37)>, 335: <ir.Block at stencil_serial_laplace.py (38)>, 336: <ir.Block at stencil_serial_laplace.py (38)>, 337: <ir.Block at stencil_serial_laplace.py (63)>, 210: <ir.Block at stencil_serial_laplace.py (37)>, 187: <ir.Block at stencil_serial_laplace.py (36)>, 190: <ir.Block at stencil_serial_laplace.py (37)>}Var($parfor_index_tuple_var.123, stencil_serial_laplace.py (63))" at stencil_serial_laplace.py (63)
Failed at nopython (nopython mode backend)
Caused By:
Traceback (most recent call last):
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 240, in run
stage()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 454, in stage_nopython_frontend
self.locals)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 867, in type_inference_stage
infer.build_constraint()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 800, in build_constraint
self.constrain_statement(inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 959, in constrain_statement
self.typeof_assign(inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 1021, in typeof_assign
self.typeof_global(inst, inst.target, value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 1147, in typeof_global
literal_value=gvar.value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 866, in lock_type
tv.lock(tp, loc=loc, literal_value=literal_value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 72, in lock
assert not self.locked
AssertionError
Failed at nopython (nopython frontend)
File "stencil_serial_laplace.py", line 63
[1] During: lowering "[LoopNest(index_variable = $parfor_index_var.79, range = (8, last_ind.114, 1)), LoopNest(index_variable = $parfor_index_var.80, range = (8, last_ind.117, 1)), LoopNest(index_variable = $parfor_index_var.81, range = (8, last_ind.120, 1))]{165: <ir.Block at stencil_serial_laplace.py (32)>, 207: <ir.Block at stencil_serial_laplace.py (37)>, 335: <ir.Block at stencil_serial_laplace.py (38)>, 336: <ir.Block at stencil_serial_laplace.py (38)>, 337: <ir.Block at stencil_serial_laplace.py (63)>, 210: <ir.Block at stencil_serial_laplace.py (37)>, 187: <ir.Block at stencil_serial_laplace.py (36)>, 190: <ir.Block at stencil_serial_laplace.py (37)>}Var($parfor_index_tuple_var.123, stencil_serial_laplace.py (63))" at stencil_serial_laplace.py (63)

Its worth nothing that I was able to use this decorator with a "manual" laplace stencil of the same mathematical definition and achieve parallelization as well as the intended final result of my computation:
@numba.njit(parallel=True)

Okay.  I understand now.  Please make the body of the loop "for i in range(1, nt):" into a function and put @njit(parallel=True) on that function.

Okay I created a function for a single timestep returning a total of 3 arrays full of values at the previous, current, and next time value. It works in serial with the expected result, but when I add the @numba.njit(parallel=True) decorator to the new  time_step function, I'm getting a similar error as before.
Relevant piece of code:
@numba.njit(parallel=True)
def time_step(next_timestep, curr_timestep, prev_timestep, source):
	next_timestep = 2*curr_timestep - prev_timestep + laplace(curr_timestep, coeff)*((dt*vel/h)**2)
	next_timestep[x_idx+radius, y_idx+radius, z_idx+radius] += source*(vel*dt)**2
	prev_timestep = curr_timestep
	curr_timestep = next_timestep
	return next_timestep, curr_timestep, prev_timestep

def all_time_step(next_timestep, curr_timestep, prev_timestep, nt):
	for i in range(1, nt):
		next_timestep, curr_timestep, prev_timestep = time_step(next_timestep, curr_timestep, prev_timestep, source[i])
	return next_timestep

next_timestep = all_time_step(next_timestep, curr_timestep, prev_timestep, nt)

Error message:

Traceback (most recent call last):
File "stencil_parallel_laplace.py", line 82, in
next_timestep = all_time_step(next_timestep, curr_timestep, prev_timestep, nt)
File "stencil_parallel_laplace.py", line 78, in all_time_step
next_timestep, curr_timestep, prev_timestep = time_step(next_timestep, curr_timestep, prev_timestep, source[i])
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/dispatcher.py", line 307, in _compile_for_args
return self.compile(tuple(argtypes))
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/dispatcher.py", line 579, in compile
cres = self._compiler.compile(args, return_type)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/dispatcher.py", line 80, in compile
flags=flags, locals=self.locals)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 766, in compile_extra
return pipeline.compile_extra(func)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 362, in compile_extra
return self._compile_bytecode()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 725, in _compile_bytecode
return self._compile_core()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 712, in _compile_core
res = pm.run(self.status)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 248, in run
raise patched_exception
numba.errors.LoweringError: Caused By:
Traceback (most recent call last):
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 240, in run
stage()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 647, in stage_nopython_backend
self._backend(lowerfn, objectmode=False)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 602, in _backend
lowered = lowerfn()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 589, in backend_nopython_mode
self.flags)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 885, in native_lowering_stage
lower.lower()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 135, in lower
self.lower_normal_function(self.fndesc)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 176, in lower_normal_function
entry_block_tail = self.lower_function_body()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 201, in lower_function_body
self.lower_block(block)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 216, in lower_block
self.lower_inst(inst)
File "/home/jrt54/anaconda2/lib/python2.7/contextlib.py", line 35, in exit
self.gen.throw(type, value, traceback)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/errors.py", line 265, in new_error_context
six.reraise(type(newerr), newerr, sys.exc_info()[2])
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/errors.py", line 259, in new_error_context
yield
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 216, in lower_block
self.lower_inst(inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/lowering.py", line 365, in lower_inst
func(self, inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/npyufunc/parfor.py", line 67, in _lower_parfor_parallel
lowerer, parfor, typemap, typingctx, targetctx, flags, {})
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/npyufunc/parfor.py", line 602, in _create_gufunc_for_parfor_body
locals)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 780, in compile_ir
lifted_from=lifted_from)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 370, in compile_ir
return self._compile_ir()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 732, in _compile_ir
return self._compile_core()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 712, in _compile_core
res = pm.run(self.status)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 248, in run
raise patched_exception
LoweringError: Caused By:
Traceback (most recent call last):
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 240, in run
stage()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 454, in stage_nopython_frontend
self.locals)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 867, in type_inference_stage
infer.build_constraint()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 800, in build_constraint
self.constrain_statement(inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 959, in constrain_statement
self.typeof_assign(inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 1021, in typeof_assign
self.typeof_global(inst, inst.target, value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 1147, in typeof_global
literal_value=gvar.value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 866, in lock_type
tv.lock(tp, loc=loc, literal_value=literal_value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 72, in lock
assert not self.locked
AssertionError
Failed at nopython (nopython frontend)
File "stencil_parallel_laplace.py", line 65
[1] During: lowering "[LoopNest(index_variable = $parfor_index_var.78, range = (8, last_ind.113, 1)), LoopNest(index_variable = $parfor_index_var.79, range = (8, last_ind.116, 1)), LoopNest(index_variable = $parfor_index_var.80, range = (8, last_ind.119, 1))]{1: <ir.Block at stencil_parallel_laplace.py (32)>, 43: <ir.Block at stencil_parallel_laplace.py (37)>, 171: <ir.Block at stencil_parallel_laplace.py (38)>, 172: <ir.Block at stencil_parallel_laplace.py (38)>, 173: <ir.Block at stencil_parallel_laplace.py (65)>, 46: <ir.Block at stencil_parallel_laplace.py (37)>, 23: <ir.Block at stencil_parallel_laplace.py (36)>, 26: <ir.Block at stencil_parallel_laplace.py (37)>}Var($parfor_index_tuple_var.122, stencil_parallel_laplace.py (65))" at stencil_parallel_laplace.py (65)
Failed at nopython (nopython mode backend)
Caused By:
Traceback (most recent call last):
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 240, in run
stage()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 454, in stage_nopython_frontend
self.locals)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/compiler.py", line 867, in type_inference_stage
infer.build_constraint()
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 800, in build_constraint
self.constrain_statement(inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 959, in constrain_statement
self.typeof_assign(inst)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 1021, in typeof_assign
self.typeof_global(inst, inst.target, value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 1147, in typeof_global
literal_value=gvar.value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 866, in lock_type
tv.lock(tp, loc=loc, literal_value=literal_value)
File "/home/jrt54/anaconda2/lib/python2.7/site-packages/numba/typeinfer.py", line 72, in lock
assert not self.locked
AssertionError
Failed at nopython (nopython frontend)
File "stencil_parallel_laplace.py", line 65
[1] During: lowering "[LoopNest(index_variable = $parfor_index_var.78, range = (8, last_ind.113, 1)), LoopNest(index_variable = $parfor_index_var.79, range = (8, last_ind.116, 1)), LoopNest(index_variable = $parfor_index_var.80, range = (8, last_ind.119, 1))]{1: <ir.Block at stencil_parallel_laplace.py (32)>, 43: <ir.Block at stencil_parallel_laplace.py (37)>, 171: <ir.Block at stencil_parallel_laplace.py (38)>, 172: <ir.Block at stencil_parallel_laplace.py (38)>, 173: <ir.Block at stencil_parallel_laplace.py (65)>, 46: <ir.Block at stencil_parallel_laplace.py (37)>, 23: <ir.Block at stencil_parallel_laplace.py (36)>, 26: <ir.Block at stencil_parallel_laplace.py (37)>}Var($parfor_index_tuple_var.122, stencil_parallel_laplace.py (65))" at stencil_parallel_laplace.py (65)

For what it's worth, the above code works in parallel with the aforementioned njit parallel decorator when I replace this line:
next_timestep = 2*curr_timestep - prev_timestep + laplace(curr_timestep, coeff)*((dt*vel/h)**2)
with this line:
next_timestep = 2*curr_timestep - prev_timestep
So the error is probably from the "laplace" stencil itself.

It looks like some typing issue.  I notice that coeff, dt, vel, and h are globals and so you won't know their type at the point that function is being compiled.  I suggest that you do not use global variables in either the stencil function or the one with njit.  Can you please try passing those variables in and see if that makes a difference?  If not, I'll try to take a look at this tomorrow in depth.  For that, if you could share the full code it would help.  thanks.

Thank you! This now runs in parallel and returns the expected result.
For what it's worth, the issue seemed to lie solely in the "coeff" array. It also works without passing dt, vel, and h.


updating to agent that supports .net 6

work left

[x] update everything to .NET 6
[ ] Update installer to include .NET 6 runtime
[ ] Update installer to remove .NET 5 runtime
[x] remove .net 5 reference from CI.yml for unit tests at start
[ ] Fix Windows Terminal PT Run Plugin unit test max file length issue (add back in unit test)

dart build built but we don't have anything with .net 6 to validate e2e

Change <TargetFramework>net5.0-windows10.0.18362.0</TargetFramework> to <TargetFramework>net6.0-windows10.0.18362.0</TargetFramework> should be enough 🚀

lets see if CI & dart just work

Signing passed.

https://marketplace.visualstudio.com/items?itemName=WixToolset.WixToolsetVisualStudio2022Extension

https://marketplace.visualstudio.com/items?itemName=WixToolset.WixToolsetVisualStudio2022Extension

Dev docs will need an update too.

failure for CI for how launcher works for some reason:
https://dev.azure.com/ms/PowerToys/_build/results?buildId=264111&view=logs&j=0f660c0a-4423-5c5d-276b-f98b39c69e13&t=1014054a-4e0c-52b0-245e-5b994b2f1048&l=924
         Microsoft (R) Build Engine version 17.0.0+c9eb9dd64 for .NET Framework
         Copyright (C) Microsoft Corporation. All rights reserved.

         Build started 1/26/2022 12:56:44 AM.
         Project "C:\a\_work\1\s\src\modules\launcher\PowerLauncher\PowerLauncher.csproj" on node 1 (Publish target(s)).
##[error]C:\a\_work\_tool\dotnet\sdk\6.0.101\Sdks\Microsoft.NET.Sdk\targets\Microsoft.PackageDependencyResolution.targets(267,5): Error NETSDK1005: Assets file 'C:\a\_work\1\s\src\modules\launcher\PowerLauncher\obj\project.assets.json' doesn't have a target for 'net5.0-windows10.0.18362.0'. Ensure that restore has run and that you have included 'net5.0-windows10.0.18362.0' in the TargetFrameworks for your project. [C:\a\_work\1\s\src\modules\launcher\PowerLauncher\PowerLauncher.csproj]
     2>C:\a\_work\_tool\dotnet\sdk\6.0.101\Sdks\Microsoft.NET.Sdk\targets\Microsoft.PackageDependencyResolution.targets(267,5): error NETSDK1005: Assets file 'C:\a\_work\1\s\src\modules\launcher\PowerLauncher\obj\project.assets.json' doesn't have a target for 'net5.0-windows10.0.18362.0'. Ensure that restore has run and that you have included 'net5.0-windows10.0.18362.0' in the TargetFrameworks for your project. [C:\a\_work\1\s\src\modules\launcher\PowerLauncher\PowerLauncher.csproj] [C:\a\_work\1\s\installer\PowerToysSetup\PowerToysInstaller.wixproj]
         Done Building Project "C:\a\_work\1\s\src\modules\launcher\PowerLauncher\PowerLauncher.csproj" (Publish target(s)) -- FAILED.

         Build FAILED.

         "C:\a\_work\1\s\src\modules\launcher\PowerLauncher\PowerLauncher.csproj" (Publish target) (1) ->
         (ResolvePackageAssets target) ->
##[error]C:\a\_work\_tool\dotnet\sdk\6.0.101\Sdks\Microsoft.NET.Sdk\targets\Microsoft.PackageDependencyResolution.targets(267,5): Error NETSDK1005: Assets file 'C:\a\_work\1\s\src\modules\launcher\PowerLauncher\obj\project.assets.json' doesn't have a target for 'net5.0-windows10.0.18362.0'. Ensure that restore has run and that you have included 'net5.0-windows10.0.18362.0' in the TargetFrameworks for your project. [C:\a\_work\1\s\src\modules\launcher\PowerLauncher\PowerLauncher.csproj]
     2>C:\a\_work\_tool\dotnet\sdk\6.0.101\Sdks\Microsoft.NET.Sdk\targets\Microsoft.PackageDependencyResolution.targets(267,5): error NETSDK1005: Assets file 'C:\a\_work\1\s\src\modules\launcher\PowerLauncher\obj\project.assets.json' doesn't have a target for 'net5.0-windows10.0.18362.0'. Ensure that restore has run and that you have included 'net5.0-windows10.0.18362.0' in the TargetFrameworks for your project. [C:\a\_work\1\s\src\modules\launcher\PowerLauncher\PowerLauncher.csproj] [C:\a\_work\1\s\installer\PowerToysSetup\PowerToysInstaller.wixproj]

             0 Warning(s)
             1 Error(s)

         Time Elapsed 00:00:01.71
##[error]installer\packages\WiX.3.11.2\tools\wix2010.targets(779,5): Error MSB3073: The command "IF NOT DEFINED IsPipeline (
call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\Common7\Tools\VsDevCmd.bat" -arch=amd64 -host_arch=amd64 -winsdk=10.0.18362.0
SET PTRoot=..\..\..\..
call "..\..\publish.cmd"
)" exited with code 1.
     2>C:\a\_work\1\s\installer\packages\WiX.3.11.2\tools\wix2010.targets(779,5): error MSB3073: The command "IF NOT DEFINED IsPipeline ( [C:\a\_work\1\s\installer\PowerToysSetup\PowerToysInstaller.wixproj]
C:\a\_work\1\s\installer\packages\WiX.3.11.2\tools\wix2010.targets(779,5): error MSB3073: call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\Common7\Tools\VsDevCmd.bat" -arch=amd64 -host_arch=amd64 -winsdk=10.0.18362.0 [C:\a\_work\1\s\installer\PowerToysSetup\PowerToysInstaller.wixproj]
C:\a\_work\1\s\installer\packages\WiX.3.11.2\tools\wix2010.targets(779,5): error MSB3073: SET PTRoot=..\..\..\.. [C:\a\_work\1\s\installer\PowerToysSetup\PowerToysInstaller.wixproj]
C:\a\_work\1\s\installer\packages\WiX.3.11.2\tools\wix2010.targets(779,5): error MSB3073: call "..\..\publish.cmd" [C:\a\_work\1\s\installer\PowerToysSetup\PowerToysInstaller.wixproj]
C:\a\_work\1\s\installer\packages\WiX.3.11.2\tools\wix2010.targets(779,5): error MSB3073: )" exited with code 1. [C:\a\_work\1\s\installer\PowerToysSetup\PowerToysInstaller.wixproj]
     2>Done Building Project "C:\a\_work\1\s\installer\PowerToysSetup\PowerToysInstaller.wixproj" (default targets) -- FAILED.
     1>Done Building Project "C:\a\_work\1\s\installer\PowerToysSetup.sln" (default targets) -- FAILED.

May be related to nugget version? https://docs.microsoft.com/en-us/dotnet/core/tools/sdk-errors/netsdk1005

This is the error:
##[error]C:\a\_work\_tool\dotnet\sdk\6.0.101\Sdks\Microsoft.NET.Sdk\targets\Microsoft.PackageDependencyResolution.targets(267,5): Error NETSDK1005: Assets file 'C:\a\_work\1\s\src\modules\launcher\PowerLauncher\obj\project.assets.json' doesn't have a target for 'net5.0-windows10.0.18362.0'. Ensure that restore has run and that you have included 'net5.0-windows10.0.18362.0' in the TargetFrameworks for your project. [C:\a\_work\1\s\src\modules\launcher\PowerLauncher\PowerLauncher.csproj]

Seems like PowerToys Run is still expecting to find .net5.0. Might it be some dependency of PowerLauncher still referencing .net 5?

Nugets should be .NET Standard but PowerLauncher still have a .NET Framework nuget.
https://github.com/microsoft/PowerToys/blob/a1bb281386c386cdcdf133419cc468740fbba732/src/modules/launcher/PowerLauncher/PowerLauncher.csproj#L111

Gave this branch a try on Visual Studio 2022 and "Developer Command Prompt for VS 2022" and it seems to build without an issue.

i could build the solution but not the installer.

From CI system directly hitting referencing the branch

full signed build as well!

@jaimecbernardo can your team take over at this point in .56 timeframe?

update branch to main and do final adjustments

STL and preview pane work are MIA from here.

Do final validation / CR?

I did an actual installer off the signing server and it works and apps work

Swap out the installer .net 5 to 6
Fix unit test

Yes, we'll take a look.

Post .NET 6 porting we should also consider nuget packages update/consolidation.
There are some Microsoft packages that still on 4.x version.

Agreed we need to revisit nuget packages.  lets get this in and then do an update to those

@crutkas
FYI: #15717

This PR is now ready for review!

I've updated the PR description with the changes.

Any reason the PlatformToolset is still v142 (VS2019) in BackgroundActivator.vcxproj?

That is a good callout I wasn’t aware of. Bet all of us still have both on our box.

Searching for "\2019" in all files returns a few references to hardcoded VS 2019 File paths:

Top two items I’m pretty sure can be nuked

Well spotted @CleanCodeDeveloper.
I mostly used Visual Studio automatic upgrade tool for this, which missed some references. I'll fix those manually.

I can build the solution now! Thanks :)

Thanks for the review. I've since tried building on a new VM with just VS 2022 and confirm it builds as well, with the latest commits.

This makes me so happy we got to this point !!!


Move everything to the Core.Monobehaviours namespace

Contributions: We are not currently taking public contributions - see our contributions policy. However, we are accepting issues and we do want your feedback.

Description
Just moved everything involving Monobehaviours from the Improbable.Gdk.Core namespace to Improbable.Gdk.Core.Monobehaviours. Also replaced the placeholder ReaderWriterImpl with the actual type, IReaderInternal, and made a few classes internal so that no public method on a public class returned it.
Tests
Not relevant
Documentation
Not relevant
Primary reviewers
@JonasImprobable

A problem came up: Currently, a reference to the EntityGameObjectLinker, of which there should be one per worker, is stored in the WorkerBase. This breaks the design of Monobehaviour support being a feature module. This could be solved by e.g. adding a static class similar to WorkerRegistry for mapping from workers or worlds to EntityGameObjectLinkers, but since it's not obvious and wasn't actually planned for this sprint, talking with @JonasImprobable we decided to put it on hold until it's time for cleanup / refactoring things like this.

Opening a new PR to actually do it


Fix: Run pkg.pr.new only if there are changes

Added a conditional check in pkg.pr.new.yml to prevent unnecessary runs when no changes are detected in the nuqs package.
Closes #655.

Thanks! Let's see if it works now.

Thanks for your guidance and patience. I really appreciate it.

It worked! Thanks for your patience too, TIL a few more things about Git and GHA 😄

It worked! Thanks for your patience too, TIL a few more things about Git and GHA 😄

We did it! Onto the next one... 🤝

Well if you feel like it, we could optimise this even more by splitting the workflow into two jobs:

Checkout with depth 2, doing the check via git, and emitting a job-level output for the skip signal
The original build + deploy, where the whole job would get skipped if not needed.

That saves setting up dependencies and building the package if we're not going to do anything with it.

Well if you feel like it, we could optimise this even more by splitting the workflow into two jobs:

Checkout with depth 2, doing the check via git, and emitting a job-level output for the skip signal

The original build + deploy (with a default checkout now), where the whole job would get skipped if not needed.

That saves setting up dependencies and building the package if we're not going to do anything with it.

I feel like it! Want me to just submit another PR when I'm done or do you want to create an issue and assign it to me?

Go for it, no need for an issue.

Go for it, no need for an issue (maybe refer to the original #655 one for reference).

The PR is ready for 'ya here.


blunderbuss: allow request_count to be set to '0' effectively disabling functionality

There is a situation where a repository doesn't want blunderbuss, but it is configured at the org level. Since there is no way to exclude the plugin from functioning on the repo, we must have some way to disable the functionality assigning reviewers. We can do this by allowing request_count to be 0
For: https://issues.redhat.com/browse/DPTP-4164

Since there is no way to exclude the plugin from functioning on the repo

This isn't true since February 2021. Check out the excluded_repos field in the plugin configuration.
https://github.com/kubernetes/test-infra/issues/20631, https://github.com/kubernetes/test-infra/pull/20707

Since there is no way to exclude the plugin from functioning on the repo

This isn't true since February 2021. Check out the excluded_repos field in the plugin configuration. kubernetes/test-infra#20631, kubernetes/test-infra#20707

Ah, thanks for pointing that out. I even went searching for something like that and couldn't find anything.
We won't need this after all then
/close

@cjwagner, I am attempting to use the excluded_repos config, but I am getting a validation error. Maybe I am missing how this is supposed to be used, but I don't see how this logic allows us to exclude a single plugin from an org's configuration for a repo, while including the rest of them.

From what I can tell the validation is incomplete and needs to be improved to account for excluded_repos. Maybe we just haven't encountered this case before.

That is my conclusion as well. I do think that fixing that validation is a better solution than what I have done with this PR, so I will take that approach instead.


Security-Large number wrong login attempts when connected to LDAP and authentication happens via LDAP

Do the following test exection:
Try with Redfish

Large number wrong login attempts when connected to LDAP and authentication happens via LDAP
Attempt minimum 1000 wrong attempts
Verify for both LDAP cleint and local authentication module stable

REST

Large number wrong login attempts when connected to LDAP and authentication happens via LDAP
Attempt minimum 1000 wrong attempts
Verify for both LDAP cleint and local authentication module stable

GUI

Large number wrong login attempts when connected to LDAP and authentication happens via LDAP
Attempt minimum 1000 wrong attempts
Verify for both LDAP cleint and local authentication module stable

@prkatti1 How to set up ldap service so that users in ldap can log in successfully

@prkatti1 How to set up ldap service so that users in ldap can log in successfully


[liburing] create a new port

What does your PR fix?
There was no port request for this library. But I think some Linux people are interested in it.
https://github.com/axboe/liburing (It requires kernel version 5.1+.)
Which triplets are supported/not supported? Have you updated the CI baseline?
Targets Linux triplets.

x64-linux

Wish other contributors who use Linux with ARM processor can check this works for those triplets.
Seems like CC/CXX/BUILD_ARCH customization is required.

arm-linux
arm64-linux

Does your PR follow the maintainer guide?
Checked.
I saw Docker images target Linux Kernel 4.x in late 2019.
Considering to check the kernel version in the portfile and warn it.

Is there anything else that needs to be done for this PR @luncliff?

Sorry for the pause, Nancy.
I asked my friend for trying this, but not getting any feedback. I checked
symbols with nm but not sure the port works correctly.
Let me check some uring samples in GitHub in the upcoming weekend.
On Thu, May 13, 2021 at 11:27 AM NancyLi1013 @.***>
wrote:

Is there anything else that needs to be done for this PR @luncliff
https://github.com/luncliff?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
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.

I tested build/link of this port with both VCPKG_LIBRARY_LINKAGE static/dynamic.
Should be like below.
user@host:build-dbg$ ldd ./test_suite
        linux-vdso.so.1 (0x00007fff1a14f000)
        liburing.so.2 => /.../vcpkg/installed/x64-linux/debug/lib/pkgconfig/../../lib/liburing.so.2 (0x00007ffbe4c1e000)
        libpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007ffbe4be5000)
        libstdc++.so.6 => /lib/x86_64-linux-gnu/libstdc++.so.6 (0x00007ffbe4a04000)
        libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007ffbe48b5000)
        libgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x00007ffbe489a000)
        libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007ffbe46a8000)
        /lib64/ld-linux-x86-64.so.2 (0x00007ffbe4d7e000)

Plus, I added usage for CMake FindPkgConfig module.
There was no existing references for FindPkgConfig integration so I wrote it.
Any suggestion for it will be welcomed. :D

@JackBoosY Yeap. with https://github.com/microsoft/vcpkg/pull/17623#issuecomment-842001199

LGTM now, thanks for your adding this new port @luncliff.

@luncliff could you make the versions changes? I apparently can't push to your branch...

@luncliff could you make the versions changes? I apparently can't push to your branch...

Of course! Thanks for the check and commit. :)
I will update the part and check import steps again.

@strega-nil-ms please ping me when it's reviewed. :)


The PascalCamelCase rule suddenly floods output with false reports

Following latest changes to the RedHat style, vale started flooding output with suggestions from the PascalCamelCase rule to wrap camel case terms in backticks. This is problematic because among few legitimate cases, a lot of these suggestions are incorrectly triggered for common Linux acronyms DNF, RPM, KVM, VPN, IP, SSH or IBM, and common project names like GNOME, IdM, MariaDB, PostgreSQL, NetworkManager, or SELinux that do not belong in backticks.
For reference, in documentation for RHEL 9 only 30% of reported terms use camel case (that is, contain both upper case and lower case letters).

1090922.txt

I've fixed the errors, sorry for the trouble. I think we should leave this issue open for now though in case there are other unintended consequences. It may also be that the rule is more trouble than it's worth. I an open to removing it :)
Also I think we should evaluate creating a "nightly" or dev release and a stable release for the Red Hat rule set. I created https://github.com/redhat-documentation/vale-at-red-hat/issues/430


focalnet_large_fl4_o365_finetuned_on_coco.pth size mismatch

Hello,
Thank you for sharing your experiment.
I am trying to train an object detection based on focalnet large from this checkpoint :
https://github.com/FocalNet/FocalNet-DINO#training
However some mismatch size is happening. It happened with the checkpoint pretrained on object 365 and then finetuned on coco dataset. I am using this config file : "DINO_4scale_focalnet_large_fl4.py" instead of  "DINO_4scale_focalnet_fl4.py" as I did not find it in the repo. I was wondering if the config file uploaded in the repo was the correct one ?
Here the message :
RuntimeError: Error(s) in loading state_dict for DINO:
        size mismatch for transformer.level_embed: copying a param with shape torch.Size([5, 256]) from checkpoint, the shape in current model is torch.Size([4, 256]).
        size mismatch for transformer.encoder.layers.0.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.encoder.layers.0.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.encoder.layers.0.self_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.encoder.layers.0.self_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for transformer.encoder.layers.1.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.encoder.layers.1.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.encoder.layers.1.self_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.encoder.layers.1.self_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for transformer.encoder.layers.2.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.encoder.layers.2.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.encoder.layers.2.self_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.encoder.layers.2.self_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for transformer.encoder.layers.3.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.encoder.layers.3.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.encoder.layers.3.self_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.encoder.layers.3.self_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for transformer.encoder.layers.4.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.encoder.layers.4.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.encoder.layers.4.self_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.encoder.layers.4.self_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for transformer.encoder.layers.5.self_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.encoder.layers.5.self_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.encoder.layers.5.self_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.encoder.layers.5.self_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for transformer.decoder.layers.0.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.decoder.layers.0.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.decoder.layers.0.cross_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.decoder.layers.0.cross_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for transformer.decoder.layers.1.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.decoder.layers.1.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.decoder.layers.1.cross_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.decoder.layers.1.cross_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for transformer.decoder.layers.2.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.decoder.layers.2.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.decoder.layers.2.cross_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.decoder.layers.2.cross_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for transformer.decoder.layers.3.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.decoder.layers.3.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.decoder.layers.3.cross_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.decoder.layers.3.cross_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for transformer.decoder.layers.4.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.decoder.layers.4.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.decoder.layers.4.cross_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.decoder.layers.4.cross_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for transformer.decoder.layers.5.cross_attn.sampling_offsets.weight: copying a param with shape torch.Size([320, 256]) from checkpoint, the shape in current model is torch.Size([256, 256]).
        size mismatch for transformer.decoder.layers.5.cross_attn.sampling_offsets.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([256]).
        size mismatch for transformer.decoder.layers.5.cross_attn.attention_weights.weight: copying a param with shape torch.Size([160, 256]) from checkpoint, the shape in current model is torch.Size([128, 256]).
        size mismatch for transformer.decoder.layers.5.cross_attn.attention_weights.bias: copying a param with shape torch.Size([160]) from checkpoint, the shape in current model is torch.Size([128]).
        size mismatch for input_proj.0.0.weight: copying a param with shape torch.Size([256, 192, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 384, 1, 1]).
        size mismatch for input_proj.1.0.weight: copying a param with shape torch.Size([256, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 768, 1, 1]).
        size mismatch for input_proj.2.0.weight: copying a param with shape torch.Size([256, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1536, 1, 1]).
        size mismatch for input_proj.3.0.weight: copying a param with shape torch.Size([256, 1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1536, 3, 3]).

Hi, @Shiro-LK , I think it is because you are using 4scale DINO config. I changed it to 5scale. Could you please try this new config:
https://github.com/FocalNet/FocalNet-DINO/blob/main/config/DINO/DINO_5scale_focalnet_large_fl4.py


Reduce the data volume of network metrics

Describe the bug
 578 connection_writer_count
 578 connection_write_rate
 578 connection_reader_count
 578 connection_read_rate
 578 connection_create_rate
 578 connection_count

See details here https://risingwave-labs.slack.com/archives/C05NPAUNXN1/p1697362033749449?thread_ts=1697359704.044369&cid=C05NPAUNXN1
Error message/log
No response
To Reproduce
No response
Expected behavior
No response
How did you deploy RisingWave?
No response
The version of RisingWave
No response
Additional context
No response

We might need to investigate the reason first: why there are so many connections 🤔


Can't Filter by ID

https://demo.bagisto.com/bagisto-49-39-4-180/admin/sales/orders?id[gt]=20

@nitishk72 Thanks for filing the issue. But if you try our latest release 0.1.4 or download the latest master zip this bug won't be there.
The release on the demo is quite older and it had some bugs.
Please try our latest release or latest master.

Please update the demo website

@nitishk72 Yeah it will be done really soon and we will notify you.

Hi @nitishk72
We have released our new version.Please download new version of bagisto.


Translate the following pages to Hindi

[ ] Reducing the risk to the team
[x] Summary
[x] Pastoral care
[x] Health advisories by Kerala government
[ ] Official resources
[ ] Control rooms in Kerala
[ ] State and district level organizations

Thanks @karthik1997 I wasn't sure how to handle regional content - were we supposed to lookup Hindi speaking states and their respective state government guidelines and merge them or have separate pages for them (incase we find them)?

@prometheansacrifice @bodhish
Rest part of Hindi is also done in this PR.
https://github.com/coronasafe/hindi/pull/15

Thanks, everyone who helped translate the remaining pages!

@karthik1997 are all pages translated?

Don't know about Tamil, rest of them are completed

@karthik1997  OK


Need an AgentQuitFromCollectingItem handler

So that a mission can be "collect that item".
This could also be used to quit after a given quantity of a given item has been collected (so that missions of the kind "collect these many red flowers, but don't collect any blue flower") can be implemented.

@FerdinandoR There is one now: https://github.com/Microsoft/malmo/blob/master/Schemas/MissionHandlers.xsd#L1503
It will be in the next release (0.16.0).
It doesn't have a quantity threshold though, which might be nice to add if people need it. Will close this issue for now.


latency - link responsiveness

latency / lags (react.js?) - I frequently have to click links more than once (like the Market link to get from the Overview page to the Markets section) to get them to work

agreed, very annoying and not unknown.   with the initial load of markets and all the requests needed to fetch their data from ethereum contracts (and the subsequent react rendering) there's been many passes at optimizations and there will be more.
i'll leave this here as a placeholder for optimizing the initial loading experience.

I've gotten used to it, but yeah it can get annoying at times, especially
for a newcomers, I'm guessing. Yep, it's a secondary issue - just wanted to
know if you were aware of it.
On Sat, Jul 18, 2015 at 4:08 AM, Scott Leonard<EMAIL_ADDRESS>wrote:

agreed, very annoying and not unknown. with the initial load of markets
and all the requests needed to fetch their data from ethereum contracts
(and the subsequent react rendering) there's been many passes at
optimizations and there will be more.
i'll leave this here as a placeholder for optimizing the initial loading
experience.
—
Reply to this email directly or view it on GitHub
https://github.com/AugurProject/augur/issues/130#issuecomment-122468936.

I believe this has been resolved via marketeer -- latency/responsiveness issues were due to excessive amounts of network traffic bogging down the browser.


Tweak the description

"A React reconciler" -> "A React renderer" :-)
Reconciler is React itself.

The word links to https://github.com/facebook/react/tree/master/packages/react-reconciler
Think we could direct to a better explaination?

The link itself is fine. The reconciler is what allows you to create a custom renderer.

The relationship between "reconciler" and "renderer" is the same as the relation between "class" and "instance".


Fix typo on CLI help

@adambabik  I don't know why the tests are failing on ubuntu. It fails locally on my mac too if you can take a look and let me know if this is a known issue, thanks.

@degrammer it's a flaky test. I will fix it soon.


Home Assistant Energy Management Gas Consumption

Since 2021.9 there's a new feature in Home Assistant (Gas Consumption config for energy management):

On a glance a user can now see all entities using energy and thus be able to calculate usage and approximate the cost.
I would like to add my Buderus device (which is integrated in this wonderful component).
However, by default the gas consumption is not showing up in the configuration:

Was someone already able to set it up? Is there maybe a way to publish a little guide in the wiki?

What device do you have?
Attach Bosch scan so I can see your setup.
For KM200 energy consumption is given in kWh and I don't know how to calculate kWh to m3 so it will be visible in gas usage. For me I have template sensor which multiplies kWh x0.77 and returns m3 because this is correction factor for me.
For CT200 I have no idea what is unit which is returned im history entries. It might be m3 or kWh but somebody with device would need to find out it.

bosch_scan.json.zip

OK, so the energy management needs a sensor with a measurement in m3.
Something like this. Doesn't work though.
`
template:

sensor:

name: "Gas consumption"
unique_id: "gas_consumption"
device_class: "gas"
unit_of_measurement: "m³"
state: >
{% set kwh = states('sensor.energy_consumption') | float %}
{% set z_value = 0.9645 | float %}
{% set heating_value = 11.3040 | float %}
{{ ( kwh/(z_value*heating_value) ) | round(1) }}
`

However, it produces the correct value:

you need state_class: total_measurement

@pszafer Thank you! I am able to display the value in Home Assistant Energy Management now.
However, the value is much too high.
The  sensor.energy_consumption captured more than 1000kWh per day:

This is far too much. According to the Buderus App the correct value would be 12.6kWh. I feel I capture the wrong sensor.
I suppose the sensor.actual_power is fitting better. Can I also use this for the template?

Somebody already had similar problem, maybe try:
{{ ( (kwh*0.01)/(z_value*heating_value) ) | round(1) }}

Ok, I think I was able to get meaningful results:
Like described here I was able to get the Integral working:
- platform: integration
  source: sensor.recording_ractualpower
  method: left
  unit: kWh
  name: energy_spent
  round: 2

with this in place I was able to set up a sensor to measure the cubic meters required for the new Energy Management Dashboard:
template:
  - sensor:
      - name: "Gas consumption"
        unique_id: "gas_consumption"
        device_class: "gas"
        state_class: "total_increasing"
        unit_of_measurement: "m³"
        state: >
          {% set kwh = states('sensor.energy_spent') | float %}
          {% set condition_value = 0.9645 | float %}
          {% set calorific_value = 11.3040 | float %}

          {{ ( kwh/(condition_value * calorific_value) ) | round(1) }}

Maybe this could work out of the box with this integration? The condition value and the calorific value are printed on my gas invoice here in Germany. How about other countries? Could this be a configuration parameter for the component?

That's very interesting, but can you check in longer period if those value are valid. What is condition value?
I have only calorific value, but as I tried to implement it it does not make any sense...
In my region we got most gas from Russia and some from other sources, not sure really, but in the end calorific_value can change from month to month. I asked users from southern Poland and they had different calorific value every month!
Similar situation is in Sweden as I was informed.
I doubt that Bosch/Buderus knows what is your calorific_value and I think module is calculating it already from m³ to kWh and correction factor is this:
{
            "id": "/heatSources/energyMonitoring/correctionFactor",
            "type": "floatValue",
            "writeable": 1,
            "recordable": 0,
            "value": 1.0,
            "unitOfMeasure": " ",
            "minValue": 0.7,
            "maxValue": 1.3
        },

I'll check over the long run. Bosch/Buderus don't know the calorific value of course. Our local gas supplier knows. An average of this value is calculated and displayed on the invoice:

It also changes over time. Z-Zahl is the equal of the condition value. And Brennwert is the calorific value. The condition value seems to be a German thing (https://de.wikipedia.org/wiki/Zustandszahl) as wikipedia doesn't provide information in any other language.

I'm pretty sure that you can't take any values from your gas bill, Gas Company kWh != Buderus kWh

Ok, I think I was able to get meaningful results: Like described here I was able to get the Integral working:
- platform: integration
  source: sensor.recording_ractualpower
  method: left
  unit: kWh
  name: energy_spent
  round: 2

with this in place I was able to set up a sensor to measure the cubic meters required for the new Energy Management Dashboard:
template:
  - sensor:
      - name: "Gas consumption"
        unique_id: "gas_consumption"
        device_class: "gas"
        state_class: "total_increasing"
        unit_of_measurement: "m³"
        state: >
          {% set kwh = states('sensor.energy_spent') | float %}
          {% set condition_value = 0.9645 | float %}
          {% set calorific_value = 11.3040 | float %}

          {{ ( kwh/(condition_value * calorific_value) ) | round(1) }}

Maybe this could work out of the box with this integration? The condition value and the calorific value are printed on my gas invoice here in Germany. How about other countries? Could this be a configuration parameter for the component?

Which sensor for energy_spent you actually using? In my CT200 integration I see only sensor.energy_central_heating, sensor.energy_hot_water, sensor.energy_temperature

@markunca @Rasenkantenstein has KM200 gateway so different device and different sensors.
For CT200 I don't have yet ideal solution. Those sensors are one day shifted as CT200 only gives data from one day before.

If there is no sensor with actual consumption, does it make sense to still use one day old data and then change these selected data by cron in statistic database to have there data at least for long term monitoring?

There is new API available which seems to be almost perfect for this usage https://github.com/home-assistant/core/pull/56607
but I can't figure it out yet, how to put those stats propely.

There is new API available which seems to be almost perfect for this usage https://github.com/home-assistant/core/pull/56607
but I can't figure it out yet, how to put those stats propely.


Azure SDK Review - [Cognitive Services Text Analytics API]

New SDK Review meeting has been requested.
Service Name: Cognitive Services Text Analytics API
Review Created By: Quentin Robinson
Review Date: 12/12/2024 02:05 PM PT
Release Plan:1414
Hero Scenarios Link: Not Provided
Architecture Diagram Link: Not Provided
Core Concepts Doc Link: Not Provided
APIView Links:
Description: Release new 2024-11-01 and 2024-11-15-preview version in SDK beta
Detailed meeting information and documents provided can be accessed here

Meeting updated by Quentin Robinson
Service Name: Cognitive Services Text Analytics API
Review Created By: Quentin Robinson
Review Date: 12/12/2024 02:05 PM PT
Hero Scenarios Link: here
Architecture Diagram Link: Not Provided
Core Concepts Doc Link: Not Provided
APIView Links: .NET,
Description: Release new 2024-11-01 and 2024-11-15-preview version in SDK beta
Detailed meeting information and documents provided can be accessed here

Cancelled by: Quentin Robinson


fix(ClusterRules): fix description chip category name

Fixes https://issues.redhat.com/browse/CCXDEV-7192

:tada: This PR is included in version 1.0.9 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Exposing extension entry points from loader?

During a conversation at the Chicago F2F, the issue of extension loading in Vulkan came up. @mtavenrath has extensive experience with this particular topic and has found a few ways in which this is a thorny problem for Vulkan applications:

It is illegal to define any app-side symbols that start with the string 'vk'. This means one can not define extension entry point symbols by name in client code. This causes a problem for libraries such as vulkan.hpp and creates friction between various modules (e.g., middleware) that may interact with a single Vulkan context.
Function pointers obtained from one instance are not valid across instances. Similarly, function pointers obtained on one device are not valid on another. Function pointers obtained for the same physical device on different logical devices are not interchangeable either.

This makes implementing extension loading correctly somewhat tricky. Apps effectively have to set up separate pointer tables for instance vs. device extensions and associate them with specific VkInstance / VkDevice objects, which leads to messy app-side code (and a very different way of calling into core vs. extension entrypoints). The trampoline code required to deal with this situation is also likely to become sub-optimal depending on how many of the requirements a given implementation decides to respect.
It would be desirable to let the loader handle this directly, by exposing all registered extension entry points and reusing the same internal trampoline code that is in place for the core API (which already deals with the same set of restrictions). This would reduce the surface area for app-side errors that manifest themselves only in less common systems (and end up requiring ugly IHV workarounds), besides potentially reducing function call overhead for extension entrypoints (compared to homegrown solutions per app) and overall making Vulkan more usable.
The conversation at the F2F (mostly between Piers Daniell, Jesse Hall, Jason Ekstrand, Markus Tavenrath and myself) seemed to indicate this would be doable. There is a caveat with apps that expect symbols to be present in the loader where an end-user system might have an older loader without those symbols, which could lead to a cryptic OS message when attempting to run the app. There seem to be several possible solutions to this problem:

Enable delay-loading at link time for the Vulkan loader and check the loader version before making any extension calls. If the version is too low, warn the user and exit. (This is possible today on Windows by checking the DLL version field, though an explicit API could be added for this.)
Enable delay-loading, add hooks to catch symbol loading errors, warn the user and exit if an error occurs.
Ship an installer for the loader as part of the prerequisites for the app, which updates the loader if the system-installed version is too old. There is precedent for this on Windows platforms, MSVC runtime installers being a very common example (and platforms like Steam handle such things automatically for games).
Check the system loader version during installation and abort if it is too old. (Linux platforms tend to have this sort of dependency version checking built in.)

I'd like to open this idea up for discussion. Would it be viable to expose registered extension entry points from the loader?

[...] Similarly, function pointers obtained on one device are not valid on another. Function pointers obtained for the same physical device on different logical devices are not interchangeable either.

Apps can use vkGetInstanceProcAddr to obtain function pointers that are usable with all VkDevices created from the same instance.

Goals during loader development (see<EMAIL_ADDRESS>for more details):

Eliminate cryptic application startup errors due to missing symbols
Valve felt quite strongly on this, but is an attractive feature in that application will be able to start and warn user if system is missing necessary extensions instead of dying in linker.
Optimal path to Vulkan functions
Using vkGetDeviceProcAddr will give you function pointer to driver function. Fastest way to get to the driver. Is specific to VkDevice. As mentioned by @polarina, can use vkGetInstanceProcAddr if you want more generic function pointer. Though the difference isn't that large, this was a very attractive feature to lots of folks in the working group.
Support layer architecture with minimal impact to perf
Allow use of API validation during development that is gone / has no impact on application running in production.
Support multiple independent "users" within a process
For example, some library the application uses could use Vulkan with it's own VkInstance with it's own extensions and layers that would be completely independent of the application.
Allow for multiple ICDs without interfering with each other
Fix / avoid issues Linux sees with OpenGL.
Explicitly declare extensions used / needed by application
Prevent hidden / unintended use of extensions. This helps eliminates failure mode where application is run on a different device than where it was developed and is trying to use extensions not supported on this device.

To provide a historical perspective on why the loader was designed to not export extension symbols, let me provide the list of reasons. If the loader had been designed to export extension symbols, the application would have to deal with all of the following:

Any application that uses an extension entrypoint would need to make sure that it is linking with a loader that supports the extension.

If linking with an older loader, the application will fail with OS-level errors.
This implies a tighter binding between the application header version and the loader version.
In the current design, it is possible for an application to run on a system with older versions of the loader.

Extensions were expected to be added and then possibly removed from the API on a regular basis.

This would mean that newer loaders could cause OS-level errors with older applications for extensions that were removed.

Exporting extension entrypoints would make it easier for applications to call into unsupported extensions because the
application does not have to fetch the function pointer from vkGet*ProcAddr.

These suggested workarounds in this issue could allow the symbols to be exported, however, they represent a paradigm shift because they would change how the loader is delivered and utilized in the ecosystem. This would require buy-in from ISVs and IHVs

Exporting the symbols from the loader forces the application to coordinate the header version with the loader more closely.
If the task of verifying that entrypoints are present is handled by the application, then extra OS-specific code will be required to handle this task and would likely turn out to be more work for the application than simply loading the symbols through Vulkan's platform-independent mechanisms.
This work could be offloaded into some other utility, but creating a loader that loads the Vulkan loader seems like a roundabout solution that violates Vulkan's aim of reducing abstractions.

Yeah, please don't have the loader export all extension symbols.  This:

If linking with an older loader, the application will fail with OS-level errors.

caused us so much trouble with GL on Linux we ended up back-tracking from exporting all symbols and instead defining a de-facto ABI when we finally got a unified loader on Linux (GLVND).  If that experience and the general quality of bug reports we've gotten from Vulkan developers has taught me anything, it's that if developers can do less work, they will.  They won't test on old loaders/drivers/etc., they won't be aware of or heed advice to use lazy-resolve linking if some other option is available and just works on their favorite test system, and they definitely won't use vkGet*ProcAddress anymore, and won't have much sympathy for Khronos/the Vulkan ecosystem maintainers pointing at short paragraphs in our 1000+ pages of spec/appendices/guidance/blogs/notes when their app ends up being crashy.  No one seems to have the time or incentive to think about stuff that isn't actively blocking their progress.
The problems listed above with the current situation don't seem that big to me.  Trampoline layers that try to seem invisible to the app aren't a good path to pursue IMHO.  That wasn't a great idea in GL, and it isn't a great idea in Vulkan.  However, each app shouldn't have to write code to build per-instance+device function tables, but that's what the "trampoline" helper should do: something very slightly higher level.  Yeah, it gets tricky with middleware, but "tricky" is better than everything mysteriously failing to load all the time.

@nsubtil, @mtavenrath, unless and until we hear otherwise from the working group, there doesn't appear to be any related work targeted for this particular repo so we'll close this issue for now. If a utility were to be developed, it's entirely possible it could be included in the SDK just as vulkan.hpp is.  Good luck!

Nuno,
You posted to the Vulkan WG:

While we’re trying to get the ecosystem group set up, I wanted to see if we could start an open discussion on a couple of issues, so I filed a github issue for one of them. The topic in question is whether we can / should start exposing registered extension entry points from the loader.

It was good that you posted the issue on github. You got reviews and feedback from Vulkan WG members as well as more detailed information from the architects and implementers of the Vulkan loader.

Given that it was closed on short order with the message below, it sounds like this is not the right venue after all. I wanted to get the WG’s input on whether there is a venue that we might be able to use for this particular conversation in the meantime.

I am not sure it was closed on short order. I do think posting to github for the original issue was the correct discussion venue. From the feedback to the original post, it didn't appear that exposing extensions from the loader was the correct path forward and the original request to modify the loader was no longer open. Hence why it was closed.
Markus' post is talking about another possible solution to create a utility. If such a utility were to be created, it wasn't believed it would be part of the loader and validation layers repo (perhaps it was prematurely assumed this would be a utility that is included with Vulkan-HPP).


[kobo] Rename kobo script to kobotoolbox

Given this:

I think we've just used "kobo" as a shorthand from the time since before I joined. But I just looked into it, and found out that "Kobo" is actually the name of the organization that hosts and maintains KoboToolbox^1. So I think we should use the full name "KoboToolbox" (or kobotoolbox for all lower case) consistently across this script, and anywhere else where we refer to the tool.

Originally posted by @rudokemper in https://github.com/ConservationMetrics/gc-scripts-hub/issues/1#issuecomment-2520499905
I think we should rename the kobo script to kobotoolbox. That is, rename the folder, script / test names, and the summary value in the script.yaml file.
That said, I'm not convinced that we need to go and rename every var that is prefixed by kobo_ in the actual script, or tests, themselves.

@IamJeffG opinions?

@IamJeffG opinions?

Frankly I vote we skip this.  Not because there's anything incorrect with your reasoning, but because we have more important things to do.
That said, you could do this in an hour and then we move on.  This task is going to get harder the more deployments we rollout (due to the 3rd bullet below), so if we do it, it should be now.
Things that would need to happen for this issue:

[ ] rename folders & scripts
[ ] wmill sync push to every deployment
[ ] re-setup Scheduled windmill triggers.  You cannot change the script name of a schedule, so need to delete and create anew.
[ ] rename tox environments

I'm not convinced that we need to go and rename every var that is prefixed by kobo_ in the actual script, or tests, themselves.

Agreed
FWIW I still do not see this new Kobotoolbox_Account resource type in the public hub: https://github.com/ConservationMetrics/gc-scripts-hub/pull/1

I hear you, but I still went ahead and did it, because I want to get this right and as you point out this early stage is the best opportunity to do so.


Can I get output_image without target_image?

Hi. I'm Heidi.
Can I get output_image without target_image?
In main.py, the results are:
results = sess.run (save_fetch, feed_dict = {inputs_raw: input_im, targets_raw: target_im, path_LR: path_lr, path_HR: path_hr}
When I do not know targets_raw, can I get ouput image?

use inference  mode

@ShiinaMitsuki
inference mode runs, but I don't find the output.
I notice that it written evaluation starts!! but no image is evaluated
Any Idea

I face the same issue as @amrbenattia


CSIP80 - how to determine the CSIP structMap?

CSIP80 states that "Each METS file must include ONE structural map  element used exactly as described here." and then follows all the requirements for structMap.
The problem with the formulation of CSIP80 is that there is no way to determine if a structMap element is meant to live up to requirements CSIP81-112. There might be two structMap elements which only partially live up to the requirements and how would a validator know which one to validate?
In the testcase https://github.com/DILCISBoard/eark-ip-test-corpus/pull/229/commits/9710696f5581df2183738bd04a7a156f85088b02 I have tried to combine CSIP82 and CSIP80 so that it is the mets/structMap[@LABEL='CSIP'] that determines that this is indeed a structMap-element that it stated to live up to all the requirements.
I suggest a reformulation of CSIP80 to something like:
Structural description of the package
mets/structMap
The <structMap> in the CSIP describes the highest logical structure of the IP.
Each METS file must include ONE structural map <structMap> element used exactly as described in the following requirements regarding the <structMap>
The way to identify the <structMap> element which follows all the structMap requirements is given in CSIP82.
Institutions can add their own additional custom structural maps as separate <structMap> sections.

Closed with PR #676


Alt Attributes missing on images

Various images across the blog homepage and blog posts are missing alt attributes, this is an accessibility WCAG compliance concern.  It can be updated after deployment, I don’t think it is a no-go situation, just something that should be addressed and audited so someone can go in and add the attribute to images that require them (non-background images).
@davidnuescheler responded back to this issue mentioned in slack:
i think this is general an authoring hygiene issue... as far as the alt texts on cards (hero images goes) the query-index.xlsx would give a comprehensive list of articles where the alt text is missing... at a quick glance ~437 article images are missing an alt text.

@aaronmauchley-adobe tracking here and assigned to Porter: DOTCOM-54822


feat(lsp port): allow setting the lsp port during dev.

Set lsp port
Description
During dev the user may want to change the lsp port.
Type of change

[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] Refactoring (no functional changes, only code improvements)
[ ] Documentation update

How to Test

Step 1: run chat in dev with REFACT_LSP_PORT="9001" npm run dev
Step 2: request to the lsp should use port 9001.

Screenshots (if applicable)

Checklist

[ ] My code follows the code style of this project.
[ ] I have performed a self-review of my code.
[ ] I have commented my code, particularly in hard-to-understand areas.
[ ] I have added tests that prove my fix is effective or that my feature works.
[ ] New and existing unit tests pass locally with my changes.
[ ] Any dependent changes have been merged and published in downstream modules.
[ ] I have updated the documentation where necessary.

Linked Issues
Additional Notes

@MarcMcIntosh you might want to change readme as well if you delete VITE_REFACT_LSP_URL


improvement add --cap-add SYS_PTRACE command for docker-dev.md

Proposed changes
add --cap-add SYS_PTRACE command for docker-dev.md
Checklist(Required)

[ ] Does it affect the original behavior
[ ] Has unit tests been added
[x] Has document been added or modified
[ ] Does it need to update dependencies
[ ] Is this PR support rollback (If NO, please explain WHY)

run buildall


updated expired test certificates

Coverage remained the same at 84.129% when pulling 50226daf95c7a15414b49cc45a1ff744b58e3479 on Tieske:master into e6d1f0d062853e96c287d1917efcac6047fbfa54 on keplerproject:master.


registry() failed if there are values with REG_EXPAND_SZ type exist in Windows registry path

I need to find value with REG_EXPAND_SZ in key in Windows registry. Package function crashed with exception:
Exception: expandPath is not defined
stack: ReferenceError: expandPath is not defined
    at RegType.parse (PROJECT_PATH\node_modules\windows\lib\registry.js:114:25)
    at new Entry (PROJECT_PATH\node_modules\windows\lib\registry.js:259:72)
    at PROJECT_PATH\node_modules\windows\lib\registry.js:55:19
    at Array.reduce (<anonymous>)
    at registry (PROJECT_PATH\node_modules\windows\lib\registry.js:35:21)
    at getV (PROJECT_PATH\app.js:30:17)

I've check the 'windows' package, function expandPath() does not exist.
I think there is a typo in registry.js:114:25:
113:  REG_EXPAND_SZ: new RegType('REG_EXPAND_SZ', {
114:   parse: function(x){ return expandPath(x) }
115:  }),

It should be just 'expand' based on line registry.js:7
7: var expand = require('./utility').expand;

Any help with it?

Thanks for the bug report!
I've taken a look and you are correct, this should have been using the expand function to expand any environmental variables referenced within the value.
I've fixed this issue, and corrected a bug which happens if you attempted to read an empty REG_EXPAND_SZ  key.
I've released version 0.1.1, can you confirm this has fixed your issue?

Verified<EMAIL_ADDRESS>Issue fixed.
Thanks for rapid response!


Create Infinity.json

{
  "infinity": {
    "symbol": "∞",
    "types": {
      "countable": {
        "description": "The size of the set of natural numbers.",
        "example": "Natural numbers (1, 2, 3, ...)",
        "cardinality": "ℵ₀"
      },
      "uncountable": {
        "description": "The size of the set of real numbers.",
        "example": "Real numbers (all decimal expansions)",
        "cardinality": "2^ℵ₀"
      }
    },
    "applications": [
      {
        "field": "Calculus",
        "usage": "Limits, integrals, and derivatives."
      },
      {
        "field": "Set Theory",
        "usage": "Understanding different sizes of infinity."
      },
      {
        "field": "Physics",
        "usage": "Concepts like infinite density in black holes."
      }
    ],
    "properties": {
      "non-numeric": true,
      "cannot_be_reached": true,
      "conceptual": true
    }
  }
}

I'm a teenager like I'm 14 and I am so passionate about it would you mind checking out my project and letting me know ?


Update teleport.qasm

Use of cregs inconsistent with other example files. Updated such that the Circuit reads  [('c', 0), ('c',1),('c',2)] and not read [('c0', 0), ('c1',0),('c2',0)].

Hi Eric, I'd like to keep this example synchronized with the document. After one of us updates the corresponding figure in the document, I'll accept this change. Thanks!

A simpler solution is to accept the change under a different file name, since there would be a couple examples to change in the document otherwise.


.DS_Store files in .zip

I downloaded https://repo1.maven.org/maven2/com/floragunn/search-guard-kibana-plugin/6.2.2-10/search-guard-kibana-plugin-6.2.2-10.zip
And then checked it's content:
unzip -v search-guard-kibana-plugin-6.2.2-10.zip | grep .DS
    6148  Defl:N      417  93% 02-22-2018 20:44 e318af80  kibana/searchguard-kibana/lib/.DS_Store
    6148  Defl:N      357  94% 02-22-2018 20:44 546692b5  kibana/searchguard-kibana/public/.DS_Store

A good build system should never contains such kind of files.

You are absolutely right and we will look into this. Thanks for reporting.

@jochenkressin i am wondering how this could happen? In clean.sh (which gets called in release.sh) this should be handled properly by find . -name '.DS_Store' -type f -delete

Agreed, but we need to check.

Confirmed as a bug.


UI: Add save button

Add save button to UI - server saves current pixel data to the corresponding file.

This issue was moved to flaki/pif-editor#3


Add HCL/terraform support

Implemented according to https://www.terraform.io/language/syntax/configuration#comments.
Signed-off-by: Simon Stamm

Looks good in a glance, will review during week as I have  more time


Why is FORMAT() non-deterministic?

Surely for a given fixed format string it produces the same output?

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 6f019407-5787-6f54-1027-084e817b6b8e
Version Independent ID: a92541d0-b63a-9603-2a1f-18e210be89e4
Content: FORMAT (Transact-SQL) - SQL Server
Content Source: docs/t-sql/functions/format-transact-sql.md
Product: sql
Technology: t-sql
GitHub Login: @MikeRayMSFT
Microsoft Alias: mikeray

@Mitch-Wheat Thank you for the question.
The FORMAT function appears to be the one exception, see complete list here.
Assigning this to the author for further review.

@julieMSFT Could you please look more into this issue? Thank you! #reassign:juliemsft

@Mitch-Wheat Hello and thank you for contacting Microsoft. I am sorry for the delayed response. Please see this link for a discussion of why FORMAT() is non-deterministic, https://dba.stackexchange.com/questions/174739/format-is-a-nondeterministic-built-in-string-function-right. FORMAT() is not always deterministic because it can be used on numeric data types. Please let me know if you have a further question.

@Mitch-Wheat Hi, I will proceed with closing this item. The @notification mechanism continues to work even after an item is closed, in case further communication is needed. We hope to hear from you again.

#please-close


Define mriqc /fmriprep metadata and metrics to extract

Opening an issue so that I do not forget.
Ideally we want to havw subset of the metrics that mriqc generate to be directly viewable by the dashboard.
These should probably be added to the bagel file that can be ingested by the dash board.
Given that there are many metrics and one value for each metric for each file this can lead to many many additional columns to be added to the bagel file.
It may be better to start with a small subset of metrics to start and also implement them on the dashboard side.

Pinging @alyssadai @nikhil153 @InesGP

from @nikhil153
MRIQC metrics for dashboard:

fmri: EFC, SNR, DWARS, FD (mm), SUMMARY_BG
smri: EFC, SNR, FWHM (vox), INU, SUMMARY_BG


fastlane new version changes obscures error

Is that possible to move showing new versions information on fastlane up rather then bottom? since if you have an error it simply too hard to go up and find it, i would rather see a last message about my job status than new versions of fastlane ?

@romk1n There is not a way to move it to the beginning right now but you can disable it by set an environment variable like this FASTLANE_SKIP_UPDATE_CHECK=1 - https://docs.fastlane.tools/advanced/#skip-update-check-when-launching-fastlane

@romk1n You can also use FASTLANE_HIDE_CHANGELOG to hide more information (which does clean the output response size a bit too)


[e2e]: go schedule main config-noldflags slsa3

Repo: https://github.com/slsa-framework/example-package/tree/main
Run: https://github.com/slsa-framework/example-package/actions/runs/3005004747
Workflow file: https://github.com/slsa-framework/example-package/tree/main/.github/workflows/e2e.go.schedule.main.config-noldflags.slsa3.yml
Workflow runs: https://github.com/slsa-framework/example-package/actions/workflows/e2e.go.schedule.main.config-noldflags.slsa3.yml
Trigger: schedule
Branch: main
Date: Wed Sep  7 04:23:07 UTC 2022

Repo: https://github.com/slsa-framework/example-package/tree/main
Run: https://github.com/slsa-framework/example-package/actions/runs/3012370659
Workflow file: https://github.com/slsa-framework/example-package/tree/main/.github/workflows/e2e.go.schedule.main.config-noldflags.slsa3.yml
Workflow runs: https://github.com/slsa-framework/example-package/actions/workflows/e2e.go.schedule.main.config-noldflags.slsa3.yml
Trigger: schedule
Branch: main
Date: Thu Sep  8 04:07:34 UTC 2022

Repo: https://github.com/slsa-framework/example-package/tree/main
Run: https://github.com/slsa-framework/example-package/actions/runs/3019863474
Workflow file: https://github.com/slsa-framework/example-package/tree/main/.github/workflows/e2e.go.schedule.main.config-noldflags.slsa3.yml
Workflow runs: https://github.com/slsa-framework/example-package/actions/workflows/e2e.go.schedule.main.config-noldflags.slsa3.yml
Trigger: schedule
Branch: main
Date: Fri Sep  9 04:04:20 UTC 2022

Repo: https://github.com/slsa-framework/example-package/tree/main
Run: https://github.com/slsa-framework/example-package/actions/runs/3026631817
Workflow file: https://github.com/slsa-framework/example-package/tree/main/.github/workflows/e2e.go.schedule.main.config-noldflags.slsa3.yml
Workflow runs: https://github.com/slsa-framework/example-package/actions/workflows/e2e.go.schedule.main.config-noldflags.slsa3.yml
Trigger: schedule
Branch: main
Date: Sat Sep 10 04:13:54 UTC 2022
Tests are passing now. Closing this issue.


Recent session

@180079995 can we merge this now?

@180079995 can we merge this now?

If it look good to you.

If it look good to you.

LGTM!


Add resources to framework for dApp development

@surajondev Please add the hacktoberfest-accepted label to the PR :)


Required properties

Codecov Report

Merging #66 (fe4409e) into main (51c46df) will increase coverage by 0.00%.
The diff coverage is 88.73%.

@@           Coverage Diff           @@
##             main      #66   +/-   ##
=======================================
  Coverage   92.55%   92.55%
=======================================
  Files          64       65    +1
  Lines        3356     3397   +41
=======================================
+ Hits         3106     3144   +38
- Misses        176      177    +1
- Partials       74       76    +2

Flag
Coverage Δ

unittests
92.55% <88.73%> (+<0.01%)
:arrow_up:

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files
Coverage Δ

diff/components_diff.go
86.20% <0.00%> (-3.08%)
:arrow_down:

diff/headers_diff.go
95.83% <ø> (+6.22%)
:arrow_up:

diff/schemas_diff.go
100.00% <ø> (ø)

diff/required_diff.go
90.32% <90.32%> (ø)

diff/schema_diff.go
90.43% <92.10%> (-0.44%)
:arrow_down:

Continue to review full report at Codecov.

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GPT-4 support

As issue titled

I have just released v1.3.2 with support for multiple models, But as I don't have access to GPT-4 API personally, I am not able text if it works as intended. Can anyone with GPT-4 API check if GPT 4 shows up as an option under Advanced API Settings?

It's not showing up even though I'm having GPT4 access on the web

Do you have GPT-4 API access? I think having ChatGPT Pro subscription does not automatically give GPT-4 API access. So, even with ChatGPT Pro subscription, access to GPT-4's API through the waitlist is required.


Add ISS Tracking API to Demo

Added ISS Tracking API to demo file with some real time animation

will check it out asap, no laptop available here!

Sorry the late reply, I was traveling for a few months and my laptop broke down..
Awesome addition! This is a very cool idea. I might play around with the globe a bit more to make it easier to add custom shapes on the globe (and overall improve the API).


PacketConn interface

is it possible to add support for the udp net.PacketConn interface?

I'm not familiar with that API type. What is your use case?

a server that uses udp protocol

UDP is already supported but this project could use some tests and/or examples doing UDP as well.

using proxyproto.NewConn doesnt let me convert the proxyproto.Conn to net.PacketConn

Have a need to use it in a UDP server. Would be great to have support for this.


get_product_fees_estimate_for_sku white space issue

When a SKU (Test 123) has a white space the following error will be returned: 'No ASIN found for SKU: Test+123. Please make sure the SKU is valid.'
I've tried encoding the SKU before the request, and tried leaving the SKU as is, but I cannot get the correct output.

Yeah, had a lot of trouble with this. Just use skus without a whitespace, Amazon doesn't like them.

Just out of interest, there are similar problems with the product pricing endpoint - have a look if one of the solutions there work for you

Thanks for the reply. I am submitting the same SKU in the product pricing endpoint and it works as intended without any encoding. I tried the fee estimate request within postman without encoding the SKU and it seems to work.


Clarify the registry key setting for RPA

Clarified the registry key to use for unattended RPA scenarios

@massonj  Thanks for submitting this change to clarify the registry info.


Channel Names can be null

can you be more specific? like what do we need to change/update?

ignore until discord documents this behavior


Extending a short hand ambient module import

TypeScript Version:  2.1.1
Code
declare module 'base';

import * as Base from 'base';

class Derived extends Base { // error TS2507: Type 'any' is not a constructor function type.
}

Expected behavior:
An easy concise way of declaring a module so that the base import can be extended
Actual behavior:
error TS2507: Type 'any' is not a constructor function type.
In order to get around the error, I have to do something like this:
declare module 'base' {
  interface Constructor {
      new (...args: any[]): this;
  }

  const _: Constructor;
  export = _;
}

this is the same as:
var a: any;
class C extends a {}

the type system does not know what extending any really means. does it mean that the class is any or how would a super call be...
you can cast to a class/constructor function if you need to e.g.
declare class B { }
const constructor = a as typeof B;

class C extends constructor {
}

or just define the class on the module.

@mhegazy Those solutions are all very verbose when declaring many modules as an interim step in converting a large javascript codebase.
It would be better to have the short hand module declaration export a more inclusive any object which allowed one to extend it. I can already call new on the import, but can't extend it? e.g. the following generates no error:
import * as Base from 'base';

const base = new Base();

In my experience this is a huge point of friction for JavaScript teams working on existing code-bases dipping their toes into the statically typed world. They're almost immediately hit with the need to:

understand how to write types for 3rd party modules

understand how legacy commonjs modules are described in TS (e.g. exports = …)

understand @types/ and how to deal:

versioning
working around incorrect types
dependency vs devDependency

It would be great if instead extending an any type worked.

the type system does not know what extending any really means. does it mean that the class is any or how would a super call be...

I'd be fine with the class being an any.


ModuleNotFoundError: No module named 'fire'

Traceback (most recent call last):
File "/Users/shuyuanduann/metagpt/startup.py", line 5, in
import fire
ModuleNotFoundError: No module named 'fire'

did you run pip install requirements before?

I just wrote this up incase it'll help you get started on your project.
https://github.com/geekan/MetaGPT/issues/166

I manage to fix it:

First I did pip install -r requirements.txt
It was still failing when running with an error about bs4 (BeautifulSoup). I fixed with pip install BeautifulSoup4

I am using Python 3.11.2