[FEA] CudaSupportError: Error at driver init: Call to cuInit results in CUDA_ERROR_NO_DEVICE (100):

[maheshmrs]

This issue template is intended to be used primarily for requests related to pandas accelerator mode. If you'd like to file a general cuDF feature request, please click here.

Missing Pandas Feature Request
A clear and concise summary of the pandas function(s) you'd like to be able run with cuDF.

I was try to execute below code.

%%cudf.pandas.profile
data=pd.read_csv("training.1600000.processed.noemoticon.csv", 
                 header=None,
                encoding = "latin-1")

data = data.drop(columns=["1","2","3"])
data.columns = ["lable","user","tweet"]
data.head(3)

Profiler Output
If you used the profiler in pandas accelerator mode, please provide the full output of your profiling report.

Full Traceback

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/frame.py:775](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/frame.py#line=774), in Frame._drop_column(self, name, errors)
    774 try:
--> 775     del self._data[name]
    776 except KeyError as err:

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/column_accessor.py:155](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/column_accessor.py#line=154), in ColumnAccessor.__delitem__(self, key)
    154 old_ncols = len(self)
--> 155 del self._data[key]
    156 new_ncols = len(self)

KeyError: '3'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:928](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=927), in _fast_slow_function_call(func, *args, **kwargs)
    927 fast_args, fast_kwargs = _fast_arg(args), _fast_arg(kwargs)
--> 928 result = func(*fast_args, **fast_kwargs)
    929 if result is NotImplemented:
    930     # try slow path

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:26](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=25), in call_operator(fn, args, kwargs)
     25 def call_operator(fn, args, kwargs):
---> 26     return fn(*args, **kwargs)

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/utils/performance_tracking.py:51](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/utils/performance_tracking.py#line=50), in _performance_tracking.<locals>.wrapper(*args, **kwargs)
     44     stack.enter_context(
     45         nvtx.annotate(
     46             message=func.__qualname__,
   (...)
     49         )
     50     )
---> 51 return func(*args, **kwargs)

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/indexed_frame.py:5255](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/indexed_frame.py#line=5254), in IndexedFrame.drop(self, labels, axis, index, columns, level, inplace, errors)
   5254     for label in _get_unique_drop_labels(target):
-> 5255         out._drop_column(label, errors=errors)
   5256 elif axis in (0, "index"):

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/utils/performance_tracking.py:51](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/utils/performance_tracking.py#line=50), in _performance_tracking.<locals>.wrapper(*args, **kwargs)
     44     stack.enter_context(
     45         nvtx.annotate(
     46             message=func.__qualname__,
   (...)
     49         )
     50     )
---> 51 return func(*args, **kwargs)

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/frame.py:778](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/frame.py#line=777), in Frame._drop_column(self, name, errors)
    777 if errors != "ignore":
--> 778     raise KeyError(f"column '{name}' does not exist") from err

KeyError: "column '3' does not exist"

During handling of the above exception, another exception occurred:

CudaSupportError                          Traceback (most recent call last)
Cell In[24], line 5
      1 data=pd.read_csv("training.1600000.processed.noemoticon.csv", 
      2                  header=None,
      3                 encoding = "latin-1")
----> 5 data = data.drop(columns=["1","2","3"])
      6 data.columns = ["lable","user","tweet"]
      7 data.head(3)

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:697](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=696), in _CallableProxyMixin.__call__(self, *args, **kwargs)
    696 def __call__(self, *args, **kwargs) -> Any:
--> 697     result, _ = _fast_slow_function_call(
    698         # We cannot directly call self here because we need it to be
    699         # converted into either the fast or slow object (by
    700         # _fast_slow_function_call) to avoid infinite recursion.
    701         # TODO: When Python 3.11 is the minimum supported Python version
    702         # this can use operator.call
    703         call_operator,
    704         self,
    705         args,
    706         kwargs,
    707     )
    708     return result

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:975](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=974), in _fast_slow_function_call(func, *args, **kwargs)
    967     raise ProxyFallbackError(
    968         f"The operation failed with cuDF, the reason was {type(err)}: {err}."
    969     ) from err
    970 with nvtx.annotate(
    971     "EXECUTE_SLOW",
    972     color=_CUDF_PANDAS_NVTX_COLORS["EXECUTE_SLOW"],
    973     domain="cudf_pandas",
    974 ):
--> 975     slow_args, slow_kwargs = _slow_arg(args), _slow_arg(kwargs)
    976     if _env_get_bool("LOG_FAST_FALLBACK", False):
    977         from ._logger import log_fallback

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:1095](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=1094), in _slow_arg(arg)
   1091 """
   1092 Transform "arg" into its corresponding slow type.
   1093 """
   1094 seen: set[int] = set()
-> 1095 return _transform_arg(arg, "_fsproxy_slow", seen)

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:1014](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=1013), in _transform_arg(arg, attribute_name, seen)
   1005 elif isinstance(arg, tuple):
   1006     # This attempts to handle arbitrary subclasses of tuple by
   1007     # assuming that if you've subclassed tuple with some special
   (...)
   1010     # __getnewargs_ex__ or __getnewargs__). Perhaps this should
   1011     # use __reduce_ex__ instead...
   1012     if type(arg) is tuple:
   1013         # Must come first to avoid infinite recursion
-> 1014         return tuple(_transform_arg(a, attribute_name, seen) for a in arg)
   1015     elif hasattr(arg, "__getnewargs_ex__"):
   1016         # Partial implementation of to reconstruct with
   1017         # transformed pieces
   1018         # This handles scipy._lib._bunch._make_tuple_bunch
   1019         args, kwargs = (
   1020             _transform_arg(a, attribute_name, seen)
   1021             for a in arg.__getnewargs_ex__()
   1022         )

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:1014](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=1013), in <genexpr>(.0)
   1005 elif isinstance(arg, tuple):
   1006     # This attempts to handle arbitrary subclasses of tuple by
   1007     # assuming that if you've subclassed tuple with some special
   (...)
   1010     # __getnewargs_ex__ or __getnewargs__). Perhaps this should
   1011     # use __reduce_ex__ instead...
   1012     if type(arg) is tuple:
   1013         # Must come first to avoid infinite recursion
-> 1014         return tuple(_transform_arg(a, attribute_name, seen) for a in arg)
   1015     elif hasattr(arg, "__getnewargs_ex__"):
   1016         # Partial implementation of to reconstruct with
   1017         # transformed pieces
   1018         # This handles scipy._lib._bunch._make_tuple_bunch
   1019         args, kwargs = (
   1020             _transform_arg(a, attribute_name, seen)
   1021             for a in arg.__getnewargs_ex__()
   1022         )

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:1014](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=1013), in _transform_arg(arg, attribute_name, seen)
   1005 elif isinstance(arg, tuple):
   1006     # This attempts to handle arbitrary subclasses of tuple by
   1007     # assuming that if you've subclassed tuple with some special
   (...)
   1010     # __getnewargs_ex__ or __getnewargs__). Perhaps this should
   1011     # use __reduce_ex__ instead...
   1012     if type(arg) is tuple:
   1013         # Must come first to avoid infinite recursion
-> 1014         return tuple(_transform_arg(a, attribute_name, seen) for a in arg)
   1015     elif hasattr(arg, "__getnewargs_ex__"):
   1016         # Partial implementation of to reconstruct with
   1017         # transformed pieces
   1018         # This handles scipy._lib._bunch._make_tuple_bunch
   1019         args, kwargs = (
   1020             _transform_arg(a, attribute_name, seen)
   1021             for a in arg.__getnewargs_ex__()
   1022         )

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:1014](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=1013), in <genexpr>(.0)
   1005 elif isinstance(arg, tuple):
   1006     # This attempts to handle arbitrary subclasses of tuple by
   1007     # assuming that if you've subclassed tuple with some special
   (...)
   1010     # __getnewargs_ex__ or __getnewargs__). Perhaps this should
   1011     # use __reduce_ex__ instead...
   1012     if type(arg) is tuple:
   1013         # Must come first to avoid infinite recursion
-> 1014         return tuple(_transform_arg(a, attribute_name, seen) for a in arg)
   1015     elif hasattr(arg, "__getnewargs_ex__"):
   1016         # Partial implementation of to reconstruct with
   1017         # transformed pieces
   1018         # This handles scipy._lib._bunch._make_tuple_bunch
   1019         args, kwargs = (
   1020             _transform_arg(a, attribute_name, seen)
   1021             for a in arg.__getnewargs_ex__()
   1022         )

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:997](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=996), in _transform_arg(arg, attribute_name, seen)
    994 import numpy as np
    996 if isinstance(arg, (_FastSlowProxy, _FastSlowProxyMeta, _FunctionProxy)):
--> 997     typ = getattr(arg, attribute_name)
    998     if typ is _Unusable:
    999         raise Exception("Cannot transform _Unusable")

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:515](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=514), in _FastSlowProxy._fsproxy_slow(self)
    508 @property
    509 def _fsproxy_slow(self) -> Any:
    510     """
    511     Returns the wrapped object. If the wrapped object is of "fast"
    512     type, replaces it with the corresponding "slow" object before
    513     returning it.
    514     """
--> 515     self._fsproxy_wrapped = self._fsproxy_fast_to_slow()
    516     return self._fsproxy_wrapped

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/nvtx/nvtx.py:116](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/nvtx/nvtx.py#line=115), in annotate.__call__.<locals>.inner(*args, **kwargs)
    113 @wraps(func)
    114 def inner(*args, **kwargs):
    115     libnvtx_push_range(self.attributes, self.domain.handle)
--> 116     result = func(*args, **kwargs)
    117     libnvtx_pop_range(self.domain.handle)
    118     return result

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py:188](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py#line=187), in make_final_proxy_type.<locals>._fsproxy_fast_to_slow(self)
    179 @nvtx.annotate(
    180     "COPY_FAST_TO_SLOW",
    181     color=_CUDF_PANDAS_NVTX_COLORS["COPY_FAST_TO_SLOW"],
   (...)
    185     # if we are wrapping a fast object,
    186     # convert it to a slow one
    187     if self._fsproxy_state is _State.FAST:
--> 188         return fast_to_slow(self._fsproxy_wrapped)
    189     return self._fsproxy_wrapped

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/_wrappers/pandas.py:227](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/pandas/_wrappers/pandas.py#line=226), in <lambda>(fast)
    217 def ignore_ipython_canary_check(self, **kwargs):
    218     raise AttributeError(
    219         "_ipython_canary_method_should_not_exist_ doesn't exist"
    220     )
    223 DataFrame = make_final_proxy_type(
    224     "DataFrame",
    225     cudf.DataFrame,
    226     pd.DataFrame,
--> 227     fast_to_slow=lambda fast: fast.to_pandas(),
    228     slow_to_fast=cudf.from_pandas,
    229     additional_attributes={
    230         "__array__": array_method,
    231         "__dir__": _DataFrame__dir__,
    232         "_constructor": _FastSlowAttribute("_constructor"),
    233         "_constructor_sliced": _FastSlowAttribute("_constructor_sliced"),
    234         "_accessors": set(),
    235         "_ipython_canary_method_should_not_exist_": ignore_ipython_canary_check,
    236     },
    237 )
    240 def custom_repr_html(obj):
    241     # This custom method is need to register a html format
    242     # for ipython
    243     return _fast_slow_function_call(
    244         lambda obj: obj._repr_html_(),
    245         obj,
    246     )[0]

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/utils/performance_tracking.py:51](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/utils/performance_tracking.py#line=50), in _performance_tracking.<locals>.wrapper(*args, **kwargs)
     43 if nvtx.enabled():
     44     stack.enter_context(
     45         nvtx.annotate(
     46             message=func.__qualname__,
   (...)
     49         )
     50     )
---> 51 return func(*args, **kwargs)

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/dataframe.py:5443](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/dataframe.py#line=5442), in DataFrame.to_pandas(self, nullable, arrow_type)
   5368 """
   5369 Convert to a Pandas DataFrame.
   5370 
   (...)
   5440 dtype: object
   5441 """
   5442 out_index = self.index.to_pandas()
-> 5443 out_data = {
   5444     i: col.to_pandas(nullable=nullable, arrow_type=arrow_type)
   5445     for i, col in enumerate(self._columns)
   5446 }
   5448 out_df = pd.DataFrame(out_data, index=out_index)
   5449 out_df.columns = self._data.to_pandas_index()

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/dataframe.py:5444](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/dataframe.py#line=5443), in <dictcomp>(.0)
   5368 """
   5369 Convert to a Pandas DataFrame.
   5370 
   (...)
   5440 dtype: object
   5441 """
   5442 out_index = self.index.to_pandas()
   5443 out_data = {
-> 5444     i: col.to_pandas(nullable=nullable, arrow_type=arrow_type)
   5445     for i, col in enumerate(self._columns)
   5446 }
   5448 out_df = pd.DataFrame(out_data, index=out_index)
   5449 out_df.columns = self._data.to_pandas_index()

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/column/numerical.py:693](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/column/numerical.py#line=692), in NumericalColumn.to_pandas(self, nullable, arrow_type)
    691     return pd.Index(pandas_array, copy=False)
    692 elif self.dtype.kind in set("iuf") and not self.has_nulls():
--> 693     return pd.Index(self.values_host, copy=False)
    694 else:
    695     return super().to_pandas(nullable=nullable, arrow_type=arrow_type)

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/column/column.py:218](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/column/column.py#line=217), in ColumnBase.values_host(self)
    215     raise ValueError("Column must have no nulls.")
    217 with acquire_spill_lock():
--> 218     return self.data_array_view(mode="read").copy_to_host()

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/column/column.py:133](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/cudf/core/column/column.py#line=132), in ColumnBase.data_array_view(self, mode)
    131 else:
    132     obj = None
--> 133 return cuda.as_cuda_array(obj).view(self.dtype)

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/numba/cuda/api.py:76](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/numba/cuda/api.py#line=75), in as_cuda_array(obj, sync)
     74     raise TypeError("*obj* doesn't implement the cuda array interface.")
     75 else:
---> 76     return from_cuda_array_interface(obj.__cuda_array_interface__,
     77                                      owner=obj, sync=sync)

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/numba/cuda/cudadrv/devices.py:231](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/numba/cuda/cudadrv/devices.py#line=230), in require_context.<locals>._require_cuda_context(*args, **kws)
    229 @functools.wraps(fn)
    230 def _require_cuda_context(*args, **kws):
--> 231     with _runtime.ensure_context():
    232         return fn(*args, **kws)

File [~/miniconda3/envs/faster_pandas/lib/python3.11/contextlib.py:137](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/contextlib.py#line=136), in _GeneratorContextManager.__enter__(self)
    135 del self.args, self.kwds, self.func
    136 try:
--> 137     return next(self.gen)
    138 except StopIteration:
    139     raise RuntimeError("generator didn't yield") from None

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/numba/cuda/cudadrv/devices.py:121](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/numba/cuda/cudadrv/devices.py#line=120), in _Runtime.ensure_context(self)
    110 @contextmanager
    111 def ensure_context(self):
    112     """Ensure a CUDA context is available inside the context.
    113 
    114     On entrance, queries the CUDA driver for an active CUDA context and
   (...)
    119     any top-level Numba CUDA API.
    120     """
--> 121     with driver.get_active_context():
    122         oldctx = self._get_attached_context()
    123         newctx = self.get_or_create_context(None)

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/numba/cuda/cudadrv/driver.py:495](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/numba/cuda/cudadrv/driver.py#line=494), in _ActiveContext.__enter__(self)
    493 else:
    494     hctx = drvapi.cu_context(0)
--> 495     driver.cuCtxGetCurrent(byref(hctx))
    496     hctx = hctx if hctx.value else None
    498 if hctx is None:

File [~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/numba/cuda/cudadrv/driver.py:295](http://127.0.0.1:8888/lab/tree/~/miniconda3/envs/faster_pandas/lib/python3.11/site-packages/numba/cuda/cudadrv/driver.py#line=294), in Driver.__getattr__(self, fname)
    292 self.ensure_initialized()
    294 if self.initialization_error is not None:
--> 295     raise CudaSupportError("Error at driver init: \n%s:" %
    296                            self.initialization_error)
    298 if USE_NV_BINDING:
    299     return self._cuda_python_wrap_fn(fname)

CudaSupportError: Error at driver init: 
Call to cuInit results in CUDA_ERROR_NO_DEVICE (100):

Additional context
Add any other context, code examples, or references to existing implementations about the feature request here.

[bdice]

I see your traceback includes Call to cuInit results in CUDA_ERROR_NO_DEVICE (100):. What GPU do you have in your system? Can you provide the output of nvidia-smi? Are you using a container? If so, do you have the nvidia-container-toolkit installed?

[maheshmrs]

GPU 0: NVIDIA GeForce RTX 2050 (UUID: GPU-2b9a833b-6fb9-b25e-1833-7ff832a835eb)
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 561.17 Driver Version: 561.17 CUDA Version: 12.6 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Driver-Model | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 2050 WDDM | 00000000:01:00.0 Off | N/A |
| N/A 52C P8 4W / 30W | 2715MiB / 4096MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
+-----------------------------------------------------------------------------------------+

[maheshmrs]

do you have the nvidia-container-toolkit installed? - No
I don't use container. However, I'm using WSL2 i.e. Linux on top of Windows OS.

[gforsyth]

Hey @maheshmrs -- it looks like you are relying on auto-labeled column names, but those start from 0, not from 1 -- does this work for you?

%%cudf.pandas.profile
data=pd.read_csv("training.1600000.processed.noemoticon.csv", 
                 header=None,
                encoding = "latin-1")

data = data.drop(columns=["0", "1","2"])
data.columns = ["lable","user","tweet"]
data.head(3)

[bdice]

Can you also share the output of conda list? It seems like your GPU is new enough. RAPIDS does support WSL2.

[maheshmrs]

Name Version Build Channel

_libgcc_mutex 0.1 main
_openmp_mutex 5.1 1_gnu
anaconda-anon-usage 0.4.4 py312hfc0e8ea_100
archspec 0.2.3 pyhd3eb1b0_0
boltons 23.0.0 py312h06a4308_0
brotli-python 1.0.9 py312h6a678d5_8
bzip2 1.0.8 h5eee18b_6
c-ares 1.19.1 h5eee18b_0
ca-certificates 2024.9.24 h06a4308_0
certifi 2024.8.30 py312h06a4308_0
cffi 1.17.1 py312h1fdaa30_0
charset-normalizer 3.3.2 pyhd3eb1b0_0
conda 24.9.2 py312h06a4308_0
conda-content-trust 0.2.0 py312h06a4308_1
conda-libmamba-solver 24.9.0 pyhd3eb1b0_0
conda-package-handling 2.3.0 py312h06a4308_0
conda-package-streaming 0.10.0 py312h06a4308_0
cryptography 43.0.0 py312hdda0065_0
distro 1.9.0 py312h06a4308_0
expat 2.6.3 h6a678d5_0
fmt 9.1.0 hdb19cb5_1
frozendict 2.4.2 py312h06a4308_0
icu 73.1 h6a678d5_0
idna 3.7 py312h06a4308_0
jsonpatch 1.33 py312h06a4308_1
jsonpointer 2.1 pyhd3eb1b0_0
krb5 1.20.1 h143b758_1
ld_impl_linux-64 2.40 h12ee557_0
libarchive 3.7.4 hfab0078_0
libcurl 8.9.1 h251f7ec_0
libedit 3.1.20230828 h5eee18b_0
libev 4.33 h7f8727e_1
libffi 3.4.4 h6a678d5_1
libgcc-ng 11.2.0 h1234567_1
libgomp 11.2.0 h1234567_1
libmamba 1.5.8 hfe524e5_3
libmambapy 1.5.8 py312h2dafd23_3
libnghttp2 1.57.0 h2d74bed_0
libsolv 0.7.24 he621ea3_1
libssh2 1.11.0 h251f7ec_0
libstdcxx-ng 11.2.0 h1234567_1
libuuid 1.41.5 h5eee18b_0
libxml2 2.13.1 hfdd30dd_2
lz4-c 1.9.4 h6a678d5_1
menuinst 2.1.2 py312h06a4308_0
ncurses 6.4 h6a678d5_0
openssl 3.0.15 h5eee18b_0
packaging 24.1 py312h06a4308_0
pcre2 10.42 hebb0a14_1
pip 24.2 py312h06a4308_0
platformdirs 3.10.0 py312h06a4308_0
pluggy 1.0.0 py312h06a4308_1
pybind11-abi 5 hd3eb1b0_0
pycosat 0.6.6 py312h5eee18b_1
pycparser 2.21 pyhd3eb1b0_0
pysocks 1.7.1 py312h06a4308_0
python 3.12.7 h5148396_0
readline 8.2 h5eee18b_0
reproc 14.2.4 h6a678d5_2
reproc-cpp 14.2.4 h6a678d5_2
requests 2.32.3 py312h06a4308_0
ruamel.yaml 0.18.6 py312h5eee18b_0
ruamel.yaml.clib 0.2.8 py312h5eee18b_0
setuptools 75.1.0 py312h06a4308_0
sqlite 3.45.3 h5eee18b_0
tk 8.6.14 h39e8969_0
tqdm 4.66.5 py312he106c6f_0
truststore 0.8.0 py312h06a4308_0
tzdata 2024b h04d1e81_0
urllib3 2.2.3 py312h06a4308_0
wheel 0.44.0 py312h06a4308_0
xz 5.4.6 h5eee18b_1
yaml-cpp 0.8.0 h6a678d5_1
zlib 1.2.13 h5eee18b_1
zstandard 0.23.0 py312h2c38b39_0
zstd 1.5.6 hc292b87_0