---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/lowering.py in from_fndesc(cls, fndesc)
33 # Avoid creating new Env
---> 34 return cls._memo[fndesc.env_name]
35 except KeyError:
/opt/conda/envs/rapids/lib/python3.6/weakref.py in __getitem__(self, key)
136 self._commit_removals()
--> 137 o = self.data[key]()
138 if o is None:
KeyError: '_ZN08NumbaEnv7my_task6MyNode7process12$3clocals$3e21calculate_change$2415E5ArrayIdLi1E1A7mutable7alignedE'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-154-16346e4770d1> in <module>
11 task_graph = TaskGraph([my_task])
12 outputs = ['my_task']
---> 13 df, = task_graph.run(outputs=outputs)
/opt/conda/envs/rapids/lib/python3.6/site-packages/gquant/dataframe_flow/taskGraph.py in run(self, outputs, replace, profile)
386
387 for i in inputs:
--> 388 i.flow()
389
390 results_dfs_dict = outputs_collector_node.input_df
/opt/conda/envs/rapids/lib/python3.6/site-packages/gquant/dataframe_flow/_node_flow.py in flow(self)
467
468 inputs_data = self.__get_input_df()
--> 469 output_df = self.__call__(inputs_data)
470
471 self_has_ports = self._using_ports()
/opt/conda/envs/rapids/lib/python3.6/site-packages/gquant/dataframe_flow/_node_flow.py in __call__(self, inputs_data)
703 for ient in self.inputs]
704 if not self.delayed_process:
--> 705 output_df = self.decorate_process()(inputs)
706 else:
707 if self._using_ports():
/home/UdfBug/myNode.py in process(self, inputs)
26 df['price'] = np.array([(1 + x*1.2) for x in range(n)])
27 df = df.set_index('datetime')
---> 28 df['change'] = df['price'].rolling('3s').apply(calculate_change)
29
30 return df
/opt/conda/envs/rapids/lib/python3.6/site-packages/cudf/core/window/rolling.py in apply(self, func, *args, **kwargs)
272 "Handling UDF with null values is not yet supported"
273 )
--> 274 return self._apply_agg(func)
275
276 def _normalize(self):
/opt/conda/envs/rapids/lib/python3.6/site-packages/cudf/core/window/rolling.py in _apply_agg(self, agg_name)
230 def _apply_agg(self, agg_name):
231 if isinstance(self.obj, cudf.Series):
--> 232 return self._apply_agg_series(self.obj, agg_name)
233 else:
234 return self._apply_agg_dataframe(self.obj, agg_name)
/opt/conda/envs/rapids/lib/python3.6/site-packages/cudf/core/window/rolling.py in _apply_agg_series(self, sr, agg_name)
216 self.min_periods,
217 self.center,
--> 218 agg_name,
219 )
220 return sr._copy_construct(data=result_col)
cudf/_libxx/rolling.pyx in cudf._libxx.rolling.rolling()
cudf/_libxx/aggregation.pyx in cudf._libxx.aggregation.make_aggregation()
cudf/_libxx/aggregation.pyx in cudf._libxx.aggregation._AggregationFactory.from_udf()
/opt/conda/envs/rapids/lib/python3.6/site-packages/cudf/utils/cudautils.py in compile_udf(udf, type_signature)
289 """
290 decorated_udf = cuda.jit(udf, device=True)
--> 291 compiled = decorated_udf.compile(type_signature)
292 ptx_code = decorated_udf.inspect_ptx(type_signature).decode("utf-8")
293 output_type = numpy_support.as_dtype(compiled.signature.return_type)
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/cuda/compiler.py in compile(self, args)
109 if args not in self._compileinfos:
110 cres = compile_cuda(self.py_func, None, args, debug=self.debug,
--> 111 inline=self.inline)
112 first_definition = not self._compileinfos
113 self._compileinfos[args] = cres
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/compiler_lock.py in _acquire_compile_lock(*args, **kwargs)
30 def _acquire_compile_lock(*args, **kwargs):
31 with self:
---> 32 return func(*args, **kwargs)
33 return _acquire_compile_lock
34
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/cuda/compiler.py in compile_cuda(pyfunc, return_type, args, debug, inline)
48 return_type=return_type,
49 flags=flags,
---> 50 locals={})
51
52 library = cres.library
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/compiler.py in compile_extra(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)
549 pipeline = pipeline_class(typingctx, targetctx, library,
550 args, return_type, flags, locals)
--> 551 return pipeline.compile_extra(func)
552
553
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/compiler.py in compile_extra(self, func)
329 self.state.lifted = ()
330 self.state.lifted_from = None
--> 331 return self._compile_bytecode()
332
333 def compile_ir(self, func_ir, lifted=(), lifted_from=None):
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/compiler.py in _compile_bytecode(self)
391 """
392 assert self.state.func_ir is None
--> 393 return self._compile_core()
394
395 def _compile_ir(self):
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/compiler.py in _compile_core(self)
371 self.state.status.fail_reason = e
372 if is_final_pipeline:
--> 373 raise e
374 else:
375 raise CompilerError("All available pipelines exhausted")
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/compiler.py in _compile_core(self)
362 res = None
363 try:
--> 364 pm.run(self.state)
365 if self.state.cr is not None:
366 break
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/compiler_machinery.py in run(self, state)
345 (self.pipeline_name, pass_desc)
346 patched_exception = self._patch_error(msg, e)
--> 347 raise patched_exception
348
349 def dependency_analysis(self):
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/compiler_machinery.py in run(self, state)
336 pass_inst = _pass_registry.get(pss).pass_inst
337 if isinstance(pass_inst, CompilerPass):
--> 338 self._runPass(idx, pass_inst, state)
339 else:
340 raise BaseException("Legacy pass in use")
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/compiler_lock.py in _acquire_compile_lock(*args, **kwargs)
30 def _acquire_compile_lock(*args, **kwargs):
31 with self:
---> 32 return func(*args, **kwargs)
33 return _acquire_compile_lock
34
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/compiler_machinery.py in _runPass(self, index, pss, internal_state)
300 mutated |= check(pss.run_initialization, internal_state)
301 with SimpleTimer() as pass_time:
--> 302 mutated |= check(pss.run_pass, internal_state)
303 with SimpleTimer() as finalize_time:
304 mutated |= check(pss.run_finalizer, internal_state)
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/compiler_machinery.py in check(func, compiler_state)
273
274 def check(func, compiler_state):
--> 275 mangled = func(compiler_state)
276 if mangled not in (True, False):
277 msg = ("CompilerPass implementations should return True/False. "
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/typed_passes.py in run_pass(self, state)
405
406 # TODO: Pull this out into the pipeline
--> 407 NativeLowering().run_pass(state)
408 lowered = state['cr']
409 signature = typing.signature(state.return_type, *state.args)
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/typed_passes.py in run_pass(self, state)
346 with targetctx.push_code_library(library):
347 lower = lowering.Lower(targetctx, library, fndesc, interp,
--> 348 metadata=metadata)
349 lower.lower()
350 if not flags.no_cpython_wrapper:
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/lowering.py in __init__(self, context, library, fndesc, func_ir, metadata)
95 # Python execution environment (will be available to the compiled
96 # function).
---> 97 self.env = Environment.from_fndesc(self.fndesc)
98
99 # Internal states
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/lowering.py in from_fndesc(cls, fndesc)
34 return cls._memo[fndesc.env_name]
35 except KeyError:
---> 36 inst = cls(fndesc.lookup_globals())
37 inst.env_name = fndesc.env_name
38 cls._memo[fndesc.env_name] = inst
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/funcdesc.py in lookup_globals(self)
80 dynamically (i.e. exec)
81 """
---> 82 return self.global_dict or self.lookup_module().__dict__
83
84 def lookup_module(self):
/opt/conda/envs/rapids/lib/python3.6/site-packages/numba/funcdesc.py in lookup_module(self)
91 return _dynamic_module
92 else:
---> 93 return sys.modules[self.modname]
94
95 def lookup_function(self):
KeyError: "Failed in nopython mode pipeline (step: nopython mode backend)\n'my_task'"
Click here to see environment details
**git***
Not inside a git repository
***OS Information***
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=18.04
DISTRIB_CODENAME=bionic
DISTRIB_DESCRIPTION="Ubuntu 18.04.3 LTS"
NAME="Ubuntu"
VERSION="18.04.3 LTS (Bionic Beaver)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 18.04.3 LTS"
VERSION_ID="18.04"
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
VERSION_CODENAME=bionic
UBUNTU_CODENAME=bionic
Linux 65f3097fdf51 4.15.0-101-generic #102-Ubuntu SMP Mon May 11 10:07:26 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux
***GPU Information***
Wed Jun 3 19:35:16 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.82 Driver Version: 440.82 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 166... Off | 00000000:01:00.0 Off | N/A |
| 0% 38C P8 13W / 140W | 2104MiB / 5944MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
***CPU***
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 10
On-line CPU(s) list: 0-9
Thread(s) per core: 1
Core(s) per socket: 10
Socket(s): 1
NUMA node(s): 1
Vendor ID: AuthenticAMD
CPU family: 23
Model: 113
Model name: AMD Ryzen 5 3600 6-Core Processor
Stepping: 0
CPU MHz: 3591.870
BogoMIPS: 7183.74
Virtualization: AMD-V
L1d cache: 64K
L1i cache: 64K
L2 cache: 512K
L3 cache: 16384K
NUMA node0 CPU(s): 0-9
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw perfctr_core ssbd ibpb vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 arat npt nrip_save umip arch_capabilities
***CMake***
***g++***
***nvcc***
***Python***
/opt/conda/envs/rapids/bin/python
Python 3.6.10
***Environment Variables***
PATH : /opt/conda/envs/rapids/bin:/opt/conda/condabin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/conda/bin:/conda/bin
LD_LIBRARY_PATH : /opt/conda/envs/rapids/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/lib
NUMBAPRO_NVVM :
NUMBAPRO_LIBDEVICE :
CONDA_PREFIX : /opt/conda/envs/rapids
PYTHON_PATH :
***conda packages***
/opt/conda/condabin/conda
# packages in environment at /opt/conda/envs/rapids:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 1_llvm conda-forge
aiohttp 3.6.2 py36h516909a_0 conda-forge
appdirs 1.4.3 py_1 conda-forge
arrow-cpp 0.15.0 py36h090bef1_2 conda-forge
async-timeout 3.0.1 py_1000 conda-forge
attrs 19.3.0 py_0 conda-forge
backcall 0.1.0 py_0 conda-forge
blas 2.14 openblas conda-forge
bleach 3.1.4 pyh9f0ad1d_0 conda-forge
blosc 1.18.1 he1b5a44_0 conda-forge
bokeh 1.4.0 py36h9f0ad1d_1 conda-forge
boost 1.70.0 py36h9de70de_1 conda-forge
boost-cpp 1.70.0 h8e57a91_2 conda-forge
bqplot 0.12.12 pyh9f0ad1d_0 conda-forge
brotli 1.0.7 he1b5a44_1001 conda-forge
bzip2 1.0.8 h516909a_2 conda-forge
c-ares 1.15.0 h516909a_1001 conda-forge
ca-certificates 2020.4.5.1 hecc5488_0 conda-forge
cairo 1.16.0 hcf35c78_1003 conda-forge
certifi 2020.4.5.1 py36h9f0ad1d_0 conda-forge
cffi 1.14.0 py36hd463f26_0 conda-forge
cfitsio 3.470 hb60a0a2_2 conda-forge
chardet 3.0.4 py36h9f0ad1d_1006 conda-forge
click 7.1.1 pyh8c360ce_0 conda-forge
click-plugins 1.1.1 py_0 conda-forge
cligj 0.5.0 py_0 conda-forge
cloudpickle 1.3.0 py_0 conda-forge
colorcet 2.0.1 py_0 conda-forge
cryptography 2.8 py36h45558ae_2 conda-forge
cudatoolkit 10.2.89 h6bb024c_0 nvidia
cudf 0.13.0a200331 py36_4804 rapidsai-nightly
cudnn 7.6.5 cuda10.2_0
cugraph 0.13.0a200331 py36_415 rapidsai-nightly
cuml 0.13.0a200331 cuda10.2_py36_1351 rapidsai-nightly
cupy 7.3.0 py36h4445f8d_0 conda-forge
curl 7.68.0 hf8cf82a_0 conda-forge
cusignal 0.13.0a200331 py36_107 rapidsai-nightly
cuspatial 0.13.0a200331 py36_26 rapidsai-nightly
cuxfilter 0.13.0a200331 py36_112 rapidsai-nightly
cycler 0.10.0 py_2 conda-forge
cython 0.29.16 py36h831f99a_0 conda-forge
cytoolz 0.10.1 py36h516909a_0 conda-forge
dask 2.12.0 py_0 conda-forge
dask-core 2.12.0 py_0 conda-forge
dask-cuda 0.13.0b200331 py36_86 rapidsai-nightly
dask-cudf 0.13.0a200331 py36_4804 rapidsai-nightly
dask-glm 0.2.0 py_1 conda-forge
dask-labextension 2.0.1 py_0 conda-forge
dask-ml 1.2.0 py_0 conda-forge
dask-xgboost 0.2.0.dev28 cuda10.2py36_0 rapidsai-nightly
datashader 0.10.0 py_0 conda-forge
datashape 0.5.4 py_1 conda-forge
dbus 1.13.6 he372182_0 conda-forge
decorator 4.4.2 py_0 conda-forge
defusedxml 0.6.0 py_0 conda-forge
distributed 2.12.0 py36_0 conda-forge
dlpack 0.2 he1b5a44_1 conda-forge
double-conversion 3.1.5 he1b5a44_2 conda-forge
entrypoints 0.3 py36h9f0ad1d_1001 conda-forge
expat 2.2.9 he1b5a44_2 conda-forge
fastavro 0.23.0 py36h8c4c3a4_0 conda-forge
fastrlock 0.4 py36h831f99a_1001 conda-forge
fiona 1.8.9.post2 py36hdff7cfa_0 conda-forge
fontconfig 2.13.1 h86ecdb6_1001 conda-forge
freetype 2.10.1 he06d7ca_0 conda-forge
freexl 1.0.5 h14c3975_1002 conda-forge
fribidi 1.0.9 h516909a_0 conda-forge
fsspec 0.6.3 py_0 conda-forge
gdal 2.4.4 py36h5f563d9_0 conda-forge
geopandas 0.7.0 py_1 conda-forge
geos 3.8.0 he1b5a44_1 conda-forge
geotiff 1.5.1 h38872f0_8 conda-forge
gettext 0.19.8.1 hc5be6a0_1002 conda-forge
gflags 2.2.2 he1b5a44_1002 conda-forge
giflib 5.1.7 h516909a_1 conda-forge
git 2.26.0 pl526hf241897_0 conda-forge
glib 2.58.3 py36hd3ed26a_1003 conda-forge
glog 0.4.0 he1b5a44_1 conda-forge
gquant 0.4 pypi_0 pypi
graphite2 1.3.13 he1b5a44_1001 conda-forge
graphviz 2.42.3 h0511662_0 conda-forge
grpc-cpp 1.23.0 h18db393_0 conda-forge
gst-plugins-base 1.14.5 h0935bb2_2 conda-forge
gstreamer 1.14.5 h36ae1b5_2 conda-forge
harfbuzz 2.4.0 h9f30f68_3 conda-forge
hdf4 4.2.13 hf30be14_1003 conda-forge
hdf5 1.10.5 nompi_h3c11f04_1104 conda-forge
heapdict 1.0.1 py_0 conda-forge
icu 64.2 he1b5a44_1 conda-forge
idna 2.9 py_1 conda-forge
idna_ssl 1.1.0 py36_1000 conda-forge
imageio 2.8.0 py_0 conda-forge
importlib-metadata 1.6.0 py36h9f0ad1d_0 conda-forge
importlib_metadata 1.6.0 0 conda-forge
ipykernel 5.2.0 py36h95af2a2_1 conda-forge
ipython 7.3.0 py36h24bf2e0_0 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
ipywidgets 7.5.1 py_0 conda-forge
jedi 0.16.0 py36h9f0ad1d_1 conda-forge
jinja2 2.11.1 py_0 conda-forge
joblib 0.14.1 py_0 conda-forge
jpeg 9c h14c3975_1001 conda-forge
json-c 0.13.1 h14c3975_1001 conda-forge
json5 0.9.0 py_0 conda-forge
jsonschema 3.2.0 py36h9f0ad1d_1 conda-forge
jupyter-server-proxy 1.3.0 py_0 conda-forge
jupyter_client 6.1.2 py_0 conda-forge
jupyter_core 4.6.3 py36h9f0ad1d_1 conda-forge
jupyterlab 1.2.7 py_0 conda-forge
jupyterlab-nvdashboard 0.2.1 pypi_0 pypi
jupyterlab_server 1.1.0 py_0 conda-forge
kealib 1.4.12 hec59c27_0 conda-forge
kiwisolver 1.1.0 py36hdb11119_1 conda-forge
krb5 1.16.4 h2fd8d38_0 conda-forge
ld_impl_linux-64 2.34 h53a641e_0 conda-forge
libblas 3.8.0 14_openblas conda-forge
libcblas 3.8.0 14_openblas conda-forge
libclang 9.0.1 default_hde54327_0 conda-forge
libcudf 0.13.0a200331 cuda10.2_4804 rapidsai-nightly
libcugraph 0.13.0a200331 cuda10.2_415 rapidsai-nightly
libcuml 0.13.0a200331 cuda10.2_1351 rapidsai-nightly
libcumlprims 0.13.0a200327 cuda10.2_14 rapidsai-nightly
libcurl 7.68.0 hda55be3_0 conda-forge
libcuspatial 0.13.0a200331 cuda10.2_26 rapidsai-nightly
libdap4 3.20.4 hd3bb157_0 conda-forge
libedit 3.1.20170329 hf8c457e_1001 conda-forge
libevent 2.1.10 h72c5cf5_0 conda-forge
libffi 3.2.1 he1b5a44_1007 conda-forge
libgcc-ng 7.3.0 h24d8f2e_5 conda-forge
libgdal 2.4.4 h2b6fda6_0 conda-forge
libgfortran-ng 7.3.0 hdf63c60_5 conda-forge
libhwloc 2.1.0 h3c4fd83_0 conda-forge
libiconv 1.15 h516909a_1006 conda-forge
libkml 1.3.0 h4fcabce_1010 conda-forge
liblapack 3.8.0 14_openblas conda-forge
liblapacke 3.8.0 14_openblas conda-forge
libllvm8 8.0.1 hc9558a2_0 conda-forge
libllvm9 9.0.1 hc9558a2_0 conda-forge
libnetcdf 4.7.3 nompi_h9f9fd6a_101 conda-forge
libnvstrings 0.13.0a200331 cuda10.2_4804 rapidsai-nightly
libopenblas 0.3.7 h5ec1e0e_7 conda-forge
libpng 1.6.37 hed695b0_1 conda-forge
libpq 12.2 hae5116b_0 conda-forge
libprotobuf 3.8.0 h8b12597_0 conda-forge
librmm 0.13.0a200331 cuda10.2_567 rapidsai-nightly
libsodium 1.0.17 h516909a_0 conda-forge
libspatialindex 1.9.3 he1b5a44_3 conda-forge
libspatialite 4.3.0a ha48a99a_1034 conda-forge
libssh2 1.8.2 h22169c7_2 conda-forge
libstdcxx-ng 7.3.0 hdf63c60_5 conda-forge
libtiff 4.1.0 hfc65ed5_0 conda-forge
libtool 2.4.6 h14c3975_1002 conda-forge
libuuid 2.32.1 h14c3975_1000 conda-forge
libuv 1.34.0 h516909a_0 conda-forge
libxcb 1.13 h14c3975_1002 conda-forge
libxgboost 1.0.2dev.rapidsai0.13 cuda10.2_6 rapidsai-nightly
libxkbcommon 0.10.0 he1b5a44_0 conda-forge
libxml2 2.9.10 hee79883_0 conda-forge
llvm-openmp 9.0.1 hc9558a2_2 conda-forge
llvmlite 0.31.0 py36hfa65bc7_1 conda-forge
locket 0.2.0 py_2 conda-forge
lz4-c 1.8.3 he1b5a44_1001 conda-forge
lzo 2.10 h14c3975_1000 conda-forge
markdown 3.2.1 py_0 conda-forge
markupsafe 1.1.1 py36h8c4c3a4_1 conda-forge
matplotlib 3.2.1 0 conda-forge
matplotlib-base 3.2.1 py36hb8e4980_0 conda-forge
mistune 0.8.4 py36h516909a_1000 conda-forge
mkl 2019.5 281 conda-forge
mock 4.0.2 py36h9f0ad1d_0 conda-forge
more-itertools 8.2.0 py_0 conda-forge
msgpack-python 1.0.0 py36hdb11119_1 conda-forge
multidict 4.7.5 py36h516909a_0 conda-forge
multipledispatch 0.6.0 py_0 conda-forge
munch 2.5.0 py_0 conda-forge
nbconvert 5.6.1 py36_0 conda-forge
nbformat 5.0.4 py_0 conda-forge
nccl 2.5.7.1 hc6a2c23_0 conda-forge
ncurses 6.1 hf484d3e_1002 conda-forge
networkx 2.4 py_1 conda-forge
nodejs 13.13.0 hf5d1a2b_0 conda-forge
nomkl 3.0 0
notebook 6.0.3 py36_0 conda-forge
nspr 4.25 he1b5a44_0 conda-forge
nss 3.47 he751ad9_0 conda-forge
numba 0.48.0 py36hb3f55d8_0 conda-forge
numexpr 2.7.1 py36h830a2c2_1 conda-forge
numpy 1.18.1 py36h7314795_1 conda-forge
nvstrings 0.13.0a200331 py36_4804 rapidsai-nightly
olefile 0.46 py_0 conda-forge
openjpeg 2.3.1 h981e76c_3 conda-forge
openssl 1.1.1g h516909a_0 conda-forge
packaging 20.1 py_0 conda-forge
pandas 0.25.3 py36hb3f55d8_0 conda-forge
pandoc 2.9.2 0 conda-forge
pandocfilters 1.4.2 py_1 conda-forge
panel 0.6.4 0 conda-forge
pango 1.42.4 h7062337_4 conda-forge
param 1.9.3 py_0 conda-forge
parquet-cpp 1.5.1 2 conda-forge
parso 0.6.2 py_0 conda-forge
partd 1.1.0 py_0 conda-forge
patsy 0.5.1 py_0 conda-forge
pcre 8.44 he1b5a44_0 conda-forge
perl 5.26.2 h516909a_1006 conda-forge
pexpect 4.8.0 py36h9f0ad1d_1 conda-forge
pickleshare 0.7.5 py36h9f0ad1d_1001 conda-forge
pillow 7.0.0 py36h8328e55_1 conda-forge
pip 20.0.2 py_2 conda-forge
pixman 0.38.0 h516909a_1003 conda-forge
pluggy 0.12.0 py_0 conda-forge
poppler 0.67.0 h14e79db_8 conda-forge
poppler-data 0.4.9 1 conda-forge
postgresql 12.2 hf1211e9_0 conda-forge
proj 6.3.0 hc80f0dc_0 conda-forge
prometheus_client 0.7.1 py_0 conda-forge
prompt-toolkit 2.0.10 pypi_0 pypi
prompt_toolkit 3.0.5 0 conda-forge
psutil 5.7.0 py36h8c4c3a4_1 conda-forge
pthread-stubs 0.4 h14c3975_1001 conda-forge
ptyprocess 0.6.0 py_1001 conda-forge
py 1.8.1 py_0 conda-forge
py-xgboost 1.0.2dev.rapidsai0.13 cuda10.2py36_6 rapidsai-nightly
pyarrow 0.15.0 py36h8b68381_1 conda-forge
pycparser 2.20 py_0 conda-forge
pyct 0.4.6 py_0 conda-forge
pyct-core 0.4.6 py_0 conda-forge
pydot 1.4.1 py36h9f0ad1d_1002 conda-forge
pyee 7.0.1 py_0 conda-forge
pygments 2.6.1 py_0 conda-forge
pynvml 8.0.4 py_0 conda-forge
pyopenssl 19.1.0 py_1 conda-forge
pyparsing 2.4.6 py_0 conda-forge
pyppeteer 0.0.25 py_1 conda-forge
pyproj 2.5.0 py36h8ff28aa_0 conda-forge
pyqt 5.12.3 py36hcca6a23_1 conda-forge
pyqt5-sip 4.19.18 pypi_0 pypi
pyqtwebengine 5.12.1 pypi_0 pypi
pyrsistent 0.16.0 py36h8c4c3a4_0 conda-forge
pysocks 1.7.1 py36h9f0ad1d_1 conda-forge
pytables 3.6.1 py36h9f153d1_1 conda-forge
pytest 5.4.1 py36h9f0ad1d_0 conda-forge
python 3.6.10 h9d8adfe_1009_cpython conda-forge
python-dateutil 2.8.1 py_0 conda-forge
python-graphviz 0.14 pyh9f0ad1d_0 conda-forge
python_abi 3.6 1_cp36m conda-forge
pytz 2019.3 py_0 conda-forge
pyviz_comms 0.7.4 pyh8c360ce_0 conda-forge
pywavelets 1.1.1 py36hc1659b7_0 conda-forge
pyyaml 5.3.1 py36h8c4c3a4_0 conda-forge
pyzmq 19.0.0 py36h9947dbf_1 conda-forge
qt 5.12.5 hd8c4c69_1 conda-forge
rapids 0.13.0 cuda10.2_py36_132 rapidsai-nightly
rapids-notebook-env 0.13.0 cuda10.2_py36_0 rapidsai
rapids-xgboost 0.13.0 cuda10.2_py36_132 rapidsai-nightly
re2 2020.03.03 he1b5a44_0 conda-forge
readline 8.0 hf8c457e_0 conda-forge
requests 2.23.0 pyh8c360ce_2 conda-forge
rmm 0.13.0a200331 py36_567 rapidsai-nightly
rtree 0.9.4 py36he053a7a_1 conda-forge
scikit-image 0.16.2 py36hb3f55d8_0 conda-forge
scikit-learn 0.21.3 py36hcdab131_0 conda-forge
scipy 1.3.0 py36h921218d_1 conda-forge
seaborn 0.10.0 py_1 conda-forge
send2trash 1.5.0 py_0 conda-forge
setuptools 46.1.3 py36h9f0ad1d_0 conda-forge
shapely 1.7.0 py36hc37ca83_1 conda-forge
simpervisor 0.3 py_1 conda-forge
six 1.14.0 py_1 conda-forge
snappy 1.1.8 he1b5a44_1 conda-forge
sortedcontainers 2.1.0 py_0 conda-forge
sqlite 3.30.1 hcee41ef_0 conda-forge
statsmodels 0.11.1 py36h8c4c3a4_1 conda-forge
tblib 1.6.0 py_0 conda-forge
terminado 0.8.3 py36h9f0ad1d_1 conda-forge
testpath 0.4.4 py_0 conda-forge
thrift-cpp 0.12.0 hf3afdfd_1004 conda-forge
tk 8.6.10 hed695b0_0 conda-forge
toolz 0.10.0 py_0 conda-forge
tornado 6.0.4 py36h8c4c3a4_1 conda-forge
tqdm 4.44.1 pyh9f0ad1d_0 conda-forge
traitlets 4.3.3 py36h9f0ad1d_1 conda-forge
traittypes 0.2.1 py_1 conda-forge
typing_extensions 3.7.4.1 py36h9f0ad1d_3 conda-forge
tzcode 2019a h516909a_1002 conda-forge
ucx 1.7.0+g9d06c3a cuda10.2_0 rapidsai-nightly
ucx-py 0.13.0a200331+g9d06c3a py36_96 rapidsai-nightly
umap-learn 0.3.10 py36_1 conda-forge
uriparser 0.9.3 he1b5a44_1 conda-forge
urllib3 1.25.7 py36h9f0ad1d_1 conda-forge
wcwidth 0.1.9 pyh9f0ad1d_0 conda-forge
webencodings 0.5.1 py_1 conda-forge
websockets 8.1 py36h8c4c3a4_1 conda-forge
wheel 0.34.2 py_1 conda-forge
widgetsnbextension 3.5.1 py36_0 conda-forge
xarray 0.15.1 py_0 conda-forge
xerces-c 3.2.2 h8412b87_1004 conda-forge
xgboost 1.0.2dev.rapidsai0.13 cuda10.2py36_6 rapidsai-nightly
xorg-kbproto 1.0.7 h14c3975_1002 conda-forge
xorg-libice 1.0.10 h516909a_0 conda-forge
xorg-libsm 1.2.3 h84519dc_1000 conda-forge
xorg-libx11 1.6.9 h516909a_0 conda-forge
xorg-libxau 1.0.9 h14c3975_0 conda-forge
xorg-libxdmcp 1.1.3 h516909a_0 conda-forge
xorg-libxext 1.3.4 h516909a_0 conda-forge
xorg-libxpm 3.5.13 h516909a_0 conda-forge
xorg-libxrender 0.9.10 h516909a_1002 conda-forge
xorg-libxt 1.1.5 h516909a_1003 conda-forge
xorg-renderproto 0.11.1 h14c3975_1002 conda-forge
xorg-xextproto 7.3.0 h14c3975_1002 conda-forge
xorg-xproto 7.0.31 h14c3975_1007 conda-forge
xz 5.2.4 h516909a_1002 conda-forge
yaml 0.2.2 h516909a_1 conda-forge
yarl 1.3.0 py36h516909a_1000 conda-forge
zeromq 4.3.2 he1b5a44_2 conda-forge
zict 2.0.0 py_0 conda-forge
zipp 3.1.0 py_0 conda-forge
zlib 1.2.11 h516909a_1006 conda-forge
zstd 1.4.3 h3b9ef0a_0 conda-forge
Describe the bug
The actual RMB click menu appears very different than the screenshot animation, especially there is no [Add Nodes] entry so no way to start working at all.
Steps/Code to reproduce bug
Open notebook https://github.com/NVIDIA/fsi-samples/blob/main/gQuant/plugins/gquant_plugin/notebooks/01_tutorial.ipynb
Try follow the steps demonstrated in the screenshot animation, but right mouse button click gives a menu UI like this:
Expected behavior
To have the same or equivalent UI to accomplish the steps.
Environment overview (please complete the following information)
Environment details
Please run and paste the output of the /print_env.sh script here, to gather any other relevant environment details
N/A
Additional context
https://github.com/NVIDIA/fsi-samples/blob/main/gQuant/plugins/gquant_plugin/notebooks/cuIndicator/indicator_demo.ipynb would err out like this:
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
Input In [10], in <module>
6 task_list = [task_load_csv_data, task_sort]
7 task_graph = TaskGraph(task_list)
----> 9 df = task_graph.run(outputs=['sort.out'])[0]
11 def one_stock(df, stock_id):
12 return df.query('asset==%s' % stock_id)
File ~/anaconda3/envs/fsi/lib/python3.8/site-packages/greenflow/dataframe_flow/taskGraph.py:753, in TaskGraph.run(self, outputs, replace, profile, formated, build)
751 return result
752 else:
--> 753 return self._run(outputs=outputs, replace=replace, profile=profile,
754 formated=formated, build=build)
File ~/anaconda3/envs/fsi/lib/python3.8/site-packages/greenflow/dataframe_flow/taskGraph.py:524, in TaskGraph._run(self, outputs, replace, profile, formated, build)
521 replace = dict() if replace is None else replace
523 if build:
--> 524 self.build(replace, profile)
525 else:
526 if replace:
File ~/anaconda3/envs/fsi/lib/python3.8/site-packages/greenflow/dataframe_flow/taskGraph.py:454, in TaskGraph.build(self, replace, profile)
452 profile = False if profile is None else profile
453 # make connection only
--> 454 self._build(replace=replace, profile=profile)
456 # Columns type checking is done in the :meth:`TaskGraph._run` after the
457 # outputs are specified and participating tasks are determined.
458
459 # this part is to update each of the node so dynamic inputs can be
460 # processed
461 self.breadth_first_update()
File ~/anaconda3/envs/fsi/lib/python3.8/site-packages/greenflow/dataframe_flow/taskGraph.py:405, in TaskGraph._build(self, replace, profile)
403 node = get_node_obj(output_task, tgraph_mixin=True)
404 else:
--> 405 node = get_node_obj(task, replace.get(task_id), profile,
406 tgraph_mixin=True)
407 self.__node_dict[task_id] = node
409 # build the graph
File ~/anaconda3/envs/fsi/lib/python3.8/site-packages/greenflow/dataframe_flow/config_nodes_modules.py:249, in get_node_obj(task, replace, profile, tgraph_mixin, dask_ray_setup)
246 continue
248 if NodeClass is None:
--> 249 raise Exception("Cannot find the Node Class:" +
250 node_type)
252 if module_dir:
253 append_path(module_dir)
Exception: Cannot find the Node Class:CsvStockLoader
Due to the issue described in issue #28, it is needed to update build.sh script to use RAPIDS 0.7 images.
Update build.sh script with RAPIDS 0.9 image.
Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is. Ex. I wish I could use gQuant to do [...]
Describe the solution you'd like
A clear and concise description of what you want to happen.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Additional context
Add any other context, code examples, or references to existing implementations about the feature request here.
To compare the shape of the query signal with every position in the stream of time series.
I'm on Ubuntu 18.04.3, does this mean I have to subscribe to docker-ee to install the correct version ?
$ sudo apt install -y nvidia-docker2=2.0.3+docker18.09.7-3 nvidia-docker2=2.0.3+docker18.09.7-3
Reading package lists... Done
Building dependency tree
Reading state information... Done
Some packages could not be installed. This may mean that you have
requested an impossible situation or if you are using the unstable
distribution that some required packages have not yet been created
or been moved out of Incoming.
The following information may help to resolve the situation:
The following packages have unmet dependencies:
nvidia-docker2 : Depends: nvidia-container-runtime (= 2.0.0+docker18.09.7-3) but 3.1.4-1 is to be installed
Depends: docker-ce (= 5:18.09.7~3-0~ubuntu-bionic) but 5:19.03.5~3-0~ubuntu-bionic is to be installed or
docker-ee (= 5:18.09.7~3-0~ubuntu-bionic) but it is not installable
E: Unable to correct problems, you have held broken packages.Thanks!
Hi,
I was wondering if we might rename TaskGraph.viz_graph() method to TaskGraph.viz()?
IMO, it is redundant to specify what we are visualizing or saving, and it would also make it more consistent with other methods in that class, such us build() or run.
Please, let me know your thoughts about the above.
Regards,
Miguel
What is your question?
Is the a rolling OLS calculation available in gQuant similar to Pyfinance PandasRollingOLS?
I have a need for the rolling calculation to be performed on a GroupBy object, where a given dataframe is grouped by dates and slope calculated on a rolling basis.
Hi,
When running cuIndicator.ipynb notebook the following message is displayed in cell #5 :
KeyError: 'High'
The error is due to the dataframe series names, which need to be lowercase.
I will fix it as follows:
output = ci.ppsr(df['high'],df['low'],df['close'])
Hope it helps.
Regards,
Miguel
Congratulations on your great achievement firstly!
however in trying it out, I've been stuck at
Step 8/15 : RUN source activate rapids && conda install -y -c conda-forge dask-labextension recommonmark numpydoc sphinx_rtd_theme pudb python-graphviz bqplot=0.11.5 nodejs=11.11.0 jupyterlab=0.35.4 ipywidgets=7.4.2 pytables mkl numexpr
---> Running in aca38a804a3f
Collecting package metadata: ...working... done
Solving environment: ...working...
for several hours.
should it finally make it given I wait longer? (what's the expected time then?)
or what diagnostic steps to be taken?
sorry I'm not familiar with docker for now, but glad to learn it up with some pointers.
Thanks & Regards!
Hi,
Some notebooks refer to gQuant as a framework.
I think we should avoid that word, and use alternative expressions such as 'gQuant is a set of examples' or similar.
Regards,
Miguel
At docker/build.sh file, there are some dependencies that are already installed by other dependencies, and others that can be moved from pip to conda.
Building JupyterLab when an extension is installed can be deferred, improving container building time.
Hi,
There is a couple of debug print statements in barPlotNode.py and cumReturnNode.py files.
I will remove them.
Regards,
Miguel
Hi,
In 04_portfolio_trade.ipynb sample notebook, it seems that the number of filtered stocks in strategy_cached (#1558), differs from what it is stated in the paragraph below (#4598).
I think the text figure should be changed to match the computation.
Regards,
Miguel
Is your feature request related to a problem? Please describe.
When building the container, it takes a long time to resolve conda dependencies.
I have found that issue before. It looks like we have, at any moment, introduced a dependency that makes conda to take a long time to resolve.
Describe the solution you'd like
Make it much faster.
Additional context
I will assign this issue to me because I have worked before in optimising that container.
At time of writing, gquant_plugin assumes rapids=0.19:
https://github.com/NVIDIA/fsi-samples/blob/main/gQuant/plugins/gquant_plugin/README.md#install-the-external-example-plugin
Install RAPIDS:
conda install -y -c rapidsai -c nvidia -c conda-forge -c defaults rapids=0.19
I tried to create a virtual env for rapids=0.19 with conda create -n fsi -c rapidsai -c nvidia -c conda-forge rapids=0.19 cudatoolkit, but conda just can't solve it even after several hours of running.
Blindly tried with CUDA 11.5 and rapids 21.12, jupyter-lab just won't start.
$ conda list | grep rapids
# packages in environment at /workspace/anaconda3/envs/rapids:
cucim 21.12.00 cuda_11_py37_g6d1f082_0 rapidsai
cudf 21.12.02 cuda_11_py37_g06540b9b37_0 rapidsai
cudf_kafka 21.12.02 py37_g06540b9b37_0 rapidsai
cugraph 21.12.00 cuda11_py37_g3a43e9d0_0 rapidsai
cuml 21.12.00 cuda11_py37_g04c4927f3_0 rapidsai
cusignal 21.12.00 py37_g2bf865c_0 rapidsai
cuspatial 21.12.00 py37_gab6748f_0 rapidsai
custreamz 21.12.02 py37_g06540b9b37_0 rapidsai
cuxfilter 21.12.00 py37_g2e0fb5a_0 rapidsai
dask-cuda 21.12.00 py37_0 rapidsai
dask-cudf 21.12.02 cuda_11_py37_g06540b9b37_0 rapidsai
faiss-proc 1.0.0 cuda rapidsai
libcucim 21.12.00 cuda11_g6d1f082_0 rapidsai
libcudf 21.12.02 cuda11_g06540b9b37_0 rapidsai
libcudf_kafka 21.12.02 g06540b9b37_0 rapidsai
libcugraph 21.12.00 cuda11_g3a43e9d0_0 rapidsai
libcuml 21.12.00 cuda11_g04c4927f3_0 rapidsai
libcuspatial 21.12.00 cuda11_gab6748f_0 rapidsai
librmm 21.12.00 cuda11_g957ad04_0 rapidsai
libxgboost 1.5.0dev.rapidsai21.12 cuda11.2_0 rapidsai
ptxcompiler 0.2.0 py37h81e21aa_0 rapidsai
py-xgboost 1.5.0dev.rapidsai21.12 cuda11.2py37_0 rapidsai
rapids 21.12.00 cuda11.5_py37_gc46440c_94 rapidsai
rapids-xgboost 21.12.00 cuda11.5_py37_gc46440c_94 rapidsai
rmm 21.12.00 cuda11_py37_g957ad04_0_has_cma rapidsai
ucx 1.11.2+gef2bbcf cuda11.2_0 rapidsai
ucx-proc 1.0.0 gpu rapidsai
ucx-py 0.23.0 py37_gef2bbcf_0 rapidsai
xgboost 1.5.0dev.rapidsai21.12 cuda11.2py37_0 rapidsai
$ MODULEPATH=$PWD/modules jupyter-lab --allow-root --ip=0.0.0.0 --no-browser --NotebookApp.token=''
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/notebookapp.py:73: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
_("Don't open the notebook in a browser after startup.")
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/notebookapp.py:89: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
_("Allow the notebook to be run from root user.")
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/traits.py:20: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
help=_('Deprecated: Use minified JS file or not, mainly use during dev to avoid JS recompilation'),
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/traits.py:25: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
help=_("Supply extra arguments that will be passed to Jinja environment."))
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/traits.py:29: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
help=_("Extra variables to supply to jinja templates when rendering."),
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/traits.py:62: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
help=_("""Path to search for custom.js, css""")
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/traits.py:76: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
Can be used to override templates from notebook.templates.""")
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/traits.py:85: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
help=_("""extra paths to look for Javascript notebook extensions""")
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/traits.py:130: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
help=_("""The MathJax.js configuration file that is to be used.""")
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/traits.py:143: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
help=(_("Dict of Python modules to load as notebook server extensions."
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/notebookapp.py:124: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
This launches a Tornado based HTML Notebook Server that serves up an HTML5/Javascript Notebook client.""")
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/notebookapp.py:143: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
help=_("""Path to search for custom.js, css""")
/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/notebookapp.py:155: FutureWarning: The alias `_()` will be deprecated. Use `_i18n()` instead.
help=_("""extra paths to look for Javascript notebook extensions""")
NumExpr defaulting to 8 threads.
[I 2022-01-14 21:40:27.832 ServerApp] greenflowlab | extension was successfully linked.
[I 2022-01-14 21:40:27.832 ServerApp] jupyter_server_proxy | extension was successfully linked.
[W 2022-01-14 21:40:27.836 LabApp] 'token' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.
[I 2022-01-14 21:40:27.842 ServerApp] jupyterlab | extension was successfully linked.
[W 2022-01-14 21:40:27.857 ServerApp] 'ExtensionManager' object has no attribute '_extensions'
Traceback (most recent call last):
File "/workspace/anaconda3/envs/rapids/bin/jupyter-lab", line 10, in <module>
sys.exit(main())
File "/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/jupyter_server/extension/application.py", line 567, in launch_instance
serverapp = cls.initialize_server(argv=args)
File "/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/jupyter_server/extension/application.py", line 540, in initialize_server
find_extensions=find_extensions,
File "/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/traitlets/config/application.py", line 88, in inner
return method(app, *args, **kwargs)
File "/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/jupyter_server/serverapp.py", line 2315, in initialize
point = self.extension_manager.extension_points[starter_extension]
File "/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/jupyter_server/extension/manager.py", line 303, in extension_points
for value in self.extensions.values()
File "/workspace/anaconda3/envs/rapids/lib/python3.7/site-packages/nbclassic/nbserver.py", line 80, in extensions
nb = self._extensions.get("nbclassic")
AttributeError: 'ExtensionManager' object has no attribute '_extensions'Describe the bug
Most of the notebooks invokes, in the first cell, the download_data.sh script, to download the test dataset if needed.
It seems that download_data.sh is not available anymore in the container.
Steps/Code to reproduce bug
Try to execute the first cell of the tutorial notebook.
Expected behavior
The test dataset should be downloaded.
Environment overview (please complete the following information)
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider)]
- Method of gQuant install: [Docker build, or from source]
Environment details
Please run and paste the output of the /print_env.sh script here, to gather any other relevant environment details
Additional context
Add any other context about the problem here.
refactor it to make it as a separate module
Hi,
I have just noticed that, if the node detailed in the replace variable does not exist, gQuant does not complain at all.
It has taken me a few minutes to figure out why my workflow was failing.
It would be great if we might add an error message or at least a warning if the nodes are not present.
I would vote for raising an error. Warnings might be disabled, and difficult to trace.
Looking for your comments.
Regards,
Miguel
The fractional difference is very useful in financial time series analysis. It can retain the memory as much as possible while taking out the non-stationarity.
This is one GPU cudf implementation. Let's migrate it into a gQuant node.
We need to have a dedicated validation module to validate the types in the input/output ports. If perhaps there's a different type for inputs/outputs, or custom type, user's should be able to implement their own validation routines pluggable into the Nodes API.
Describe the bug
The cuIndicator demo notebook has various issues to reproduce its result.
Steps/Code to reproduce bug
First, an identified issue and possible fix:
#154 (comment)
Then
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Input In [16], in <module>
17 return df.query('datetime<@end_date and datetime>=@beg_date')
19 indicator_lists = ['Accumulation Distribution', 'ADMI', 'Average True Range', 'Bollinger Bands',
20 'Chaikin Oscillator', 'Commodity Channel Index', 'Coppock Curve', 'Donchian Channel',
21 'Ease of Movement', 'EWA', 'Force Index', 'Keltner Channel', 'KST Oscillator', 'MA', 'MACD',
22 'Mass Index', 'Momentum', 'Money Flow Index', 'On Balance Volume', 'Parabolic SAR',
23 'Rate of Change', 'RSI', 'Stochastic Oscillator D', 'Stochastic Oscillator K', 'TRIX',
24 'True Strength Index', 'Ultimate Oscillator', 'Vortex Indicator',]
---> 26 task_stocks_list = [task_stock_symbol]
27 task_stocks_graph = TaskGraph(task_stocks_list)
28 list_stocks = task_stocks_graph.run(outputs=['stock_symbol.stock_name'])[0].to_pandas().set_index('asset_name').to_dict()['asset']
NameError: name 'task_stock_symbol' is not defined
(I tried to give some value to that variable but further strange errors occurred, so maybe someone familiar with it should better have a look)
Expected behavior
The notebook should be reproducible.
Environment overview (please complete the following information)
Environment details
N/A
Additional context
#154
Overview
Thanks for publishing this, it's very interesting and I appreciate the time and effort you're spending on it. I'm trying to get started with 02_single_stock_trade.ipnyb with git tag v1.0.2 (abf9d33) and when I load the notebook in Jupyter Lab, I get the following error in the task_graph.draw() cell.
Any suggestions to get the graph widget working?
Error displaying widget: model not found
[5]: task_graph.run(formated=True)
Traceback (most recent call last):
Traceback (most recent call last):
File "/home/jeff/miniconda3/envs/jupyter/lib/python3.8/site-packages/gquant/dataframe_flow/taskGraph.py", line 634, in run
result = self._run(outputs=outputs, replace=replace,
File "/home/jeff/miniconda3/envs/jupyter/lib/python3.8/site-packages/gquant/dataframe_flow/taskGraph.py", line 476, in _run
self.build(replace, profile)
File "/home/jeff/miniconda3/envs/jupyter/lib/python3.8/site-packages/gquant/dataframe_flow/taskGraph.py", line 400, in build
node = task.get_node_obj(replace.get(task_id), profile,
File "/home/jeff/miniconda3/envs/jupyter/lib/python3.8/site-packages/gquant/dataframe_flow/task.py", line 184, in get_node_obj
modules = get_gquant_config_modules()
File "/home/jeff/miniconda3/envs/jupyter/lib/python3.8/site-packages/gquant/dataframe_flow/task.py", line 45, in get_gquant_config_modules
modules_list = {imod: config['ModuleFiles'][imod]
File "/home/jeff/miniconda3/envs/jupyter/lib/python3.8/site-packages/gquant/dataframe_flow/task.py", line 45, in <dictcomp>
modules_list = {imod: config['ModuleFiles'][imod]
File "/home/jeff/miniconda3/envs/jupyter/lib/python3.8/configparser.py", line 1255, in __getitem__
return self._parser.get(self._name, key)
File "/home/jeff/miniconda3/envs/jupyter/lib/python3.8/configparser.py", line 799, in get
return self._interpolation.before_get(self, section, option, value,
File "/home/jeff/miniconda3/envs/jupyter/lib/python3.8/configparser.py", line 395, in before_get
self._interpolate_some(parser, option, L, value, section, defaults, 1)
File "/home/jeff/miniconda3/envs/jupyter/lib/python3.8/configparser.py", line 434, in _interpolate_some
raise InterpolationMissingOptionError(
configparser.InterpolationMissingOptionError: Bad value substitution: option 'my_node' in section 'ModuleFiles' contains an interpolation key 'modulepath' which is not a valid option name. Raw value: '%(MODULEPATH)s/my_node.py'
Environment:
Debian 10.8 amd64
❯ jupyter serverextension list
config dir: /home/jeff/miniconda3/envs/jupyter/etc/jupyter
jupyter_server_proxy enabled
- Validating...
jupyter_server_proxy OK
jupyterlab enabled
- Validating...
jupyterlab 3.0.7 OK
❯ jupyter labextension list
JupyterLab v3.0.7
/home/jeff/miniconda3/envs/jupyter/share/jupyter/labextensions
bqplot v0.5.22 enabled OK
gquantlab v1.0.0 enabled OK
@pyviz/jupyterlab_pyviz v2.0.1 enabled OK (python, pyviz_comms)
@jupyter-widgets/jupyterlab-manager v3.0.0 enabled OK (python, jupyterlab_widgets)
conda info
conda info
active environment : jupyter
active env location : /home/jeff/miniconda3/envs/jupyter
shell level : 2
user config file : /home/jeff/.condarc
populated config files :
conda version : 4.9.2
conda-build version : not installed
python version : 3.8.5.final.0
virtual packages : __cuda=11.2=0
__glibc=2.28=0
__unix=0=0
__archspec=1=x86_64
base environment : /home/jeff/miniconda3 (writable)
channel URLs : https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /home/jeff/miniconda3/pkgs
/home/jeff/.conda/pkgs
envs directories : /home/jeff/miniconda3/envs
/home/jeff/.conda/envs
platform : linux-64
user-agent : conda/4.9.2 requests/2.24.0 CPython/3.8.5 Linux/4.19.0-14-amd64 debian/10 glibc/2.28
UID:GID : 3001:3001
netrc file : None
offline mode : False
conda list
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setuptools 49.6.0 py38h578d9bd_3 conda-forge
shapely 1.7.1 py38ha11d057_1 conda-forge
simpervisor 0.4 pyhd8ed1ab_0 conda-forge
six 1.15.0 pyh9f0ad1d_0 conda-forge
snappy 1.1.8 he1b5a44_3 conda-forge
sniffio 1.2.0 py38h578d9bd_1 conda-forge
sortedcontainers 2.3.0 pyhd8ed1ab_0 conda-forge
spdlog 1.7.0 hc9558a2_2 conda-forge
sqlite 3.33.0 h62c20be_0
streamz 0.6.2 pyh44b312d_0 conda-forge
tables 3.6.1 pypi_0 pypi
tabulate 0.8.7 pypi_0 pypi
tbb 2020.2 h4bd325d_3 conda-forge
tblib 1.7.0 pypi_0 pypi
tensorboardx 2.1 pypi_0 pypi
terminado 0.9.2 py38h578d9bd_0 conda-forge
testpath 0.4.4 py_0 conda-forge
threadpoolctl 2.1.0 pyh5ca1d4c_0 conda-forge
tiledb 1.7.7 h8efa9f0_3 conda-forge
tk 8.6.10 hbc83047_0
toolz 0.11.1 py_0 conda-forge
tornado 6.1 py38h497a2fe_1 conda-forge
tqdm 4.56.2 pyhd8ed1ab_0 conda-forge
traitlets 5.0.5 py_0 conda-forge
traittypes 0.2.1 pypi_0 pypi
treelite 0.93 py38hadf7658_3 conda-forge
treelite-runtime 0.93 pypi_0 pypi
typing-extensions 3.7.4.3 0 conda-forge
typing_extensions 3.7.4.3 py_0 conda-forge
tzcode 2021a h7f98852_0 conda-forge
ucx 1.8.1+g6b29558 cuda11.0_0 rapidsai
ucx-proc 1.0.0 gpu rapidsai
ucx-py 0.17.0 py38_g6b29558_0 rapidsai
urllib3 1.26.3 pyhd8ed1ab_0 conda-forge
wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge
webencodings 0.5.1 pypi_0 pypi
websockets 8.1 py38h497a2fe_3 conda-forge
wheel 0.36.2 pyhd3eb1b0_0
widgetsnbextension 3.5.1 py38h578d9bd_4 conda-forge
xarray 0.16.2 pyhd8ed1ab_0 conda-forge
xerces-c 3.2.2 h8412b87_1004 conda-forge
xgboost 1.3.0dev.rapidsai0.17 cuda11.0py38_0 rapidsai
xorg-kbproto 1.0.7 h7f98852_1002 conda-forge
xorg-libice 1.0.10 h516909a_0 conda-forge
xorg-libsm 1.2.3 h84519dc_1000 conda-forge
xorg-libx11 1.6.12 h516909a_0 conda-forge
xorg-libxau 1.0.9 h7f98852_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xorg-libxext 1.3.4 h516909a_0 conda-forge
xorg-libxrender 0.9.10 h516909a_1002 conda-forge
xorg-renderproto 0.11.1 h14c3975_1002 conda-forge
xorg-xextproto 7.3.0 h7f98852_1002 conda-forge
xorg-xproto 7.0.31 h7f98852_1007 conda-forge
xz 5.2.5 h7b6447c_0
yaml 0.2.5 h516909a_0 conda-forge
yarl 1.6.3 py38h497a2fe_1 conda-forge
zeromq 4.3.4 h9c3ff4c_0 conda-forge
zict 2.0.0 pypi_0 pypi
zipp 3.4.0 py_0 conda-forge
zlib 1.2.11 h7b6447c_3
zstd 1.4.8 hdf46e1d_0 conda-forge
Hi,
When running cuIndicator.ipynb notebook the following message is displayed in cell #3:
FileNotFoundError: File .cache/node_csvdata.hdf5 does not exist
I would sugget to perform the following change to fix the issue described above:
action = "load" if os.path.isfile('./.cache/node_csvdata.hdf5') else "save"
df = run([node_csv, node_sort], ['node_sort'], {'node_csvdata': {action: True}})[0]
Hope it helps!
Regards,
Miguel
Hi,
I have noticed we are using pandas.read_csv() instead of cudf.read_csv() method.
I have run a quick performance test, and cuDF version is much faster.
Based on the above, I would suggest changing pandas.read_csv() method occurrences to cudf.read_csv().
Hope it helps.
Miguel
In case I can do a fresh install of docker, what's the steps to setup a compatible nvidia docker instead of the link citing:
How do I install 2.0 if I'm not using the latest Docker version?
thanks
Is your feature request related to a problem? Please describe.
I'd like to add the following Jupyterlab extension to gQuant container:
A JupyterLab extension for displaying dashboards of GPU usage
Describe the solution you'd like
A clear and concise description of what you want to happen.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Additional context
Add any other context, code examples, or references to existing implementations about the feature request here.
Let’s merge develop branch to master.
We need gQuant to support plugin files, which includes
- gQuant python node files
- UI file describing how the ports are displayed in the client
- UI file describing the port compatibility rules.
gQuant can scan all the plugin files/directories to load them in the server and client. Plugins should be lossely coupled.
Is your feature request related to a problem? Please describe.
Rename notebook folder to notebooks.
Describe the solution you'd like
That folder contains more than one notebook. It is also according to other rapids projects notation.
Is your feature request related to a problem? Please describe.
Add cuda 10.1.2 support
Describe the solution you'd like
Add cuda 10.1.2 support, via docker build.sh
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Additional context
Add any other context, code examples, or references to existing implementations about the feature request here.
In the _node_flow.py file, we have the following logic to handle the non-dask dataframe
output_df[oport] = [iout.compute() for iout in outputs_dly[oport]][0]
We want to generalize it as the normal dask dataframe handles delayed objects.
That's a list of delayed objects. That's just another delayed collection. A dask-cudf or dask dataframe is just a collection of dataframes and itself is a delayed like object since you can call compute on it.
So the generalization would be to return the list of delayed objects that are not necessarily a dask dataframe. We would make a class such as "gQuantDaskData" to use as a port type. Then we can handle such a delayed collection as well. Based on ports type we can return something like:
output_df[oport] = gQuantDaskData(outputs_dly[oport])
This idea would generalize our ability to handle dask distributed processing.
The npartitions is just the length of the list i.e. len(outputs_dly[oport])
Users could inherit from gQuantDaskData and set port types for their particular data. Something like:
class DistributedModel(gQuantDaskData):
pass # nothing in particular just indicates this port in/out type can be a distributed modelWe check for gQuantDaskData in delayed processing call, enforce for npartitions to match, and add that as a delayed input.
On output find the port type derived from gQuantDaskData and return. Above example:
output_df[oport] = DistributedModel(outputs_dly[oport])
Is your feature request related to a problem? Please describe.
The task graph can be run in different contexts. Some of the hyper-parameters used inside the nodes are coming from this context. Currently, gQuant is not context-aware.
Describe the solution you'd like
Build a driver class to define the running environment for the graph. So the context-related information can be queried by the graph.
Hi,
It seems that the path to the datasets is incorrect at cuIndicator.ipynb notebook.
Currently, it is defined as follows:
"path": "/Project/data/stocks/stock_price_hist.csv.gz"
I think it should be:
"path": "../data/stock_price_hist.csv.gz"
Something similar is happening with the following dataset:
"path": "/Project/data/stocks/security_master.csv.gz"
Hope it helps!
Miguel
Is your feature request related to a problem? Please describe.
Currently, the example Notebooks are not using the new input/output port API, which is not supported for the UI tool.
Describe the solution you'd like
Migrate all the existing gQuant nodes to use the new input/output port API
Is your feature request related to a problem? Please describe.
Each task graph node has static typed input/output ports. This can be visually represented by the Web Dom elements. A UI tool can help to guild the user to build the graph and eliminate the errors.
Describe the solution you'd like
Build a simple server to host the REST API so the task graph data can be feeded to the client.
Build the Web UI to translate the data into Dom elements.
Hi,
I have made some changes to notebook 01_tutorial.ipynb trying to make it easier to follow.
I hope you like it.
Miguel
Hi,
After integrating with RAPIDS v0.8, it seems that dask computation is failing.
It can be reproduced in 04_portfolio_trade.ipynb notebook.
Regards,
Miguel
Is your feature request related to a problem? Please describe.
The translate_column is useful but it could be factored out to just be a node helper function and we can add examples of how to use that in columns_setup API. People can then use it or implement their own logic for dynamic columns manipulation. By factoring it out we wouldn't be coupling a mini language (DSL - domain-specific lang.) to the node class to manipulate columns.
Hi,
I have made some changes to 04_portfolio_trade.ipynb notebook trying to make it easier to follow.
I hope you like it.
Miguel
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