I tried the following
> my.exp <- RunUMAP(my.exp, dims = 1:30)
UMAP(a=None, angular_rp_forest=False, b=None, init='spectral',
learning_rate=1.0, local_connectivity=1, metric='correlation',
metric_kwds=None, min_dist=0.3, n_components=2, n_epochs=None,
n_neighbors=30, negative_sample_rate=5, random_state=None,
repulsion_strength=1.0, set_op_mix_ratio=1.0, spread=1.0,
target_metric='categorical', target_metric_kwds=None,
target_n_neighbors=-1, target_weight=0.5, transform_queue_size=4.0,
transform_seed=42, verbose=True)
Construct fuzzy simplicial set
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*** caught segfault ***
address 0xfffffffffffffffa, cause 'memory not mapped'
Traceback:
1: py_call_impl(callable, dots$args, dots$keywords)
2: umap$fit_transform(as.matrix(x = object))
3: RunUMAP.default(object = data.use, assay = assay, n.neighbors = n.neighbors, n.components = n.components, metric = metric, n.epochs = n.epochs, learning.rate = learning.rate, min.dist = min.dist, spread = spread, set.op.mix.ratio = set.op.mix.ratio, local.connectivity = local.connectivity, repulsion.strength = repulsion.strength, negative.sample.rate = negative.sample.rate, a = a, b = b, seed.use = seed.use, metric.kwds = metric.kwds, angular.rp.forest = angular.rp.forest, reduction.key = reduction.key, verbose = verbose)
4: RunUMAP(object = data.use, assay = assay, n.neighbors = n.neighbors, n.components = n.components, metric = metric, n.epochs = n.epochs, learning.rate = learning.rate, min.dist = min.dist, spread = spread, set.op.mix.ratio = set.op.mix.ratio, local.connectivity = local.connectivity, repulsion.strength = repulsion.strength, negative.sample.rate = negative.sample.rate, a = a, b = b, seed.use = seed.use, metric.kwds = metric.kwds, angular.rp.forest = angular.rp.forest, reduction.key = reduction.key, verbose = verbose)
5: RunUMAP.Seurat(my.exp, dims = 1:30)
6: RunUMAP(my.exp, dims = 1:30)
I do not see a reason why it should be getting a sigfault here. I have run this function multiple times over last several months. This seems to be happening since about a week.
Any help is appreciated.
UPDATE: I have now restarted the machine one, removed entire older R installation, started with a fresh install. I am still getting exactly same error, including address 0xffffffffffffffffffa ....
Sameet
Related
Currently I'm tryin to convert given onnx file to tensorrt file, and do inference on the generated tensorrt file.
To do so, I used tensorrt python binding API, but
"Error Code 1: Cuda Driver (invalid resource handle)" happens and there is no kind description about this.
Can anyone help me to overcome this situation?
Thx in advance, and below is my code snippet.
def trt_export(self):
fp_16_mode = True
## Obviously, I provided appropriate file names
trt_file_name = "PATH_TO_TRT_FILE"
onnx_name = "PATH_TO_ONNX_FILE"
TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE)
EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network(EXPLICIT_BATCH)
parser = trt.OnnxParser(network, TRT_LOGGER)
config = builder.create_builder_config()
config.max_workspace_size = (1<<30)
config.set_flag(trt.BuilderFlag.FP16)
config.default_device_type = trt.DeviceType.GPU
profile = builder.create_optimization_profile()
profile.set_shape('input', (1, 3, IMG_SIZE, IMG_SIZE), (12, 3, IMG_SIZE, IMG_SIZE), (32, 3, IMG_SIZE, IMG_SIZE)) # random nubmers for min. opt. max batch
config.add_optimization_profile(profile)
with open(onnx_name, 'rb') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
engine = builder.build_engine(network, config)
buf = engine.serialize()
with open(trt_file_name, 'wb') as f:
f.write(buf)
def validate_trt_result(self, input_path):
TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE)
trt_file_name = "PATH_TO_TRT_FILE"
trt_runtime = trt.Runtime(TRT_LOGGER)
with open(trt_file_name, 'rb') as f:
engine_data = f.read()
engine = trt_runtime.deserialize_cuda_engine(engine_data)
cuda.init()
device = cuda.Device(0)
ctx = device.make_context()
inputs, outputs, bindings = [], [], []
context = engine.create_execution_context()
stream = cuda.Stream()
index = 0
for binding in engine:
size = trt.volume(engine.get_binding_shape(binding)) * -1 # assuming one batch
dtype = trt.nptype(engine.get_binding_dtype(binding))
host_mem = cuda.pagelocked_empty(size, dtype)
device_mem = cuda.mem_alloc(host_mem.nbytes)
bindings.append(int(device_mem))
if engine.binding_is_input(binding):
inputs.append(HostDeviceMem(host_mem, device_mem))
context.set_binding_shape(index, [1, 3, IMG_SIZE, IMG_SIZE])
else:
outputs.append(HostDeviceMem(host_mem, device_mem))
index += 1
print(context.all_binding_shapes_specified)
input_img = cv2.imread(input_path)
input_r = cv2.resize(input_img, dsize = (256, 256))
input_p = np.transpose(input_r, (2, 0, 1))
input_e = np.expand_dims(input_p, axis = 0)
input_f = input_e.astype(np.float32)
input_f /= 255
numpy_array_input = [input_f]
hosts = [input.host for input in inputs]
trt_types = [trt.int32]
for numpy_array, host, trt_types in zip(numpy_array_input, hosts, trt_types):
numpy_array = np.asarray(numpy_array).astype(trt.nptype(trt_types)).ravel()
print(numpy_array.shape)
np.copyto(host, numpy_array)
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
#### ERROR HAPPENS HERE ####
context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
#### ERROR HAPPENS HERE ####
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
stream.synchronize()
print("TRT model inference result : ")
output = outputs[0].host
for one in output :
print(one)
ctx.pop()
Looks like ctx.push() line is missing before a line with memcpy_htod_async.
Such a error can happen if TensorFlow / PyTorch is also using CUDA in parallel with TensorRT.
See the related question/answer: https://stackoverflow.com/a/73996477/5655977
I have created 20 neural network models with the exact same architecture (but different seed numbers) and I have saved them as SM1, SM2, …., SM20. I am basically tying to import these models using the joblib function and use the average model (called NN here) to apply for various analyses. When I run this in Jupyter Notebook , I get the following error. Appreciate any help.
NN1 = joblib.load('SM1.sav')
NN2 = joblib.load('SM2.sav')
NN3 = joblib.load('SM3.sav')
NN4 = joblib.load('SM4.sav')
NN5 = joblib.load('SM5.sav')
NN6 = joblib.load('SM6.sav')
NN7 = joblib.load('SM7.sav')
NN8 = joblib.load('SM8.sav')
NN9 = joblib.load('SM9.sav')
NN10 = joblib.load('SM10.sav')
NN11 = joblib.load('SM11.sav')
NN12 = joblib.load('SM12.sav')
NN13 = joblib.load('SM13.sav')
NN14 = joblib.load('SM14.sav')
NN15 = joblib.load('SM15.sav')
NN16 = joblib.load('SM16.sav')
NN17 = joblib.load('SM17.sav')
NN18 = joblib.load('SM18.sav')
NN19 = joblib.load('SM19.sav')
NN20 = joblib.load('SM20.sav')
NN = (NN1+NN2+NN3+NN4+NN5+NN6+NN7+NN8+NN9+NN10+NN11+NN12+NN13+NN14+NN15+NN16+NN17+NN18+NN19+NN20)/(20)
TypeError: unsupported operand type(s) for +: 'MLPRegressor' and 'MLPRegressor'
Here is my code to genrate sequence of numbers. The sequence starts from 3 and stops at the sqaure root of a number input by user.
num = as.numeric(readline(prompt = "Enter a number :"))
mysqrt = as.integer(sqrt(num))
myseq = seq(from = 3,to = mysqrt, by = 2)
The error I get is ->
Error in seq.default(from = 3, to = mysqrt, by = 2) :
wrong sign in 'by' argument
If i run ->
seq(3,as.integer(sqrt(25)), by = 2)
it works fine as expected.
If i run ->
num = 25
seq(3,as.integer(sqrt(num)), by = 2)
it gives the above error.
I used the periodogram() in R and I got the error message.
Warning message: object#samp.rate * seq(1, width/2) : NAs produced by
integer overflow
Here is the code I executed. And I'm using the tuneR package.
waveform <- readWave(test.wav)
maxFreq <- sampleRate/2
minFreq <- 0
periodogram(waveform, width = 131072, overlap = 0, starts = NULL, ends = NULL, taper = 0, normalize = TRUE, frqRange = c(minFreq, maxFreq))
How do I resolve this.
I am using VLMC to fit some Markov models and it dies as soon as the alphabet size reaches 28.
I thought this was due to using a single letter in the alphabet by default, but it has the same behavior with "code1char = FALSE". This is true for me on real data as well as this fake example.
library(VLMC)
# works fine
ins <- sample(seq(1,27,1),50000,replace=T)
vlmc(ins, dump = 1,threshold.gen = 2, debug = TRUE)
#core dump
ins <- sample(seq(1,28,1),50000,replace=T)
vlmc(ins, dump = 1,threshold.gen = 2, debug = TRUE)
Any ideas?
The seg fault looks like this BTW. It looks to me like the alphabet after z is being mapped to NA which is causing an array bound issue.
library(VLMC)
sc <- 10
amp <- 13
x <- round(amp*sin(seq(0,2*sc*pi,0.01)))
x <- amp + x + rpois(NROW(x),1)
length(table(x))
length(x)
vlmc(x, dump = 1,threshold.gen = 2, debug = TRUE)
vlmc: Alpha = 'abcdefghijklmnopqrstuvwxyzNANANANANA' ; |X| = 31
vlmc: ctl.dump = 4 11
vlmc: n = |data| = 6284, cutoff{prune} = 21.8865, threshold{gen} = 2
vlmc: |alphabet| = 31, alphabet = abcdefghijklmnopqrstuvwxyzNA
generating...
*** caught segfault ***
address 0x0, cause 'memory not mapped'
Traceback:
1: .C("vlmc_p", data = Data, n = n, threshold.gen = as.integer(threshold.gen), cutoff.prune = as.double(cutoff.prune), alpha.len = as.integer(alpha.len), alpha = as.character(Alpha), debug = as.integer(as.logical(debug)), dump.flags = as.integer(c(dump, ctl.dump)), size = integer(4), PACKAGE = "VLMC")
2: vlmc(x, dump = 1, threshold.gen = 2, debug = TRUE)
As the maintainer of VLMC,
I can tell you that one of the longest standing TODO entries for VLMC has been to raise the currently builtin limit of 26 for the maximal alphabet size should be raised..
Of course it it is a bug that I don't give an error message in the case of a larger alphabet, but rather pass things to C and do not check there.
The next version of VLMC will not seg.fault for this anymore.
However, I'm not yet sure I'll find the time to allow a considerably larger alphabet....
Of course I'd happily accept patches ... it's free open source software.
Best regards,
Martin Maechler, ETH Zurich