[REVIEW] Update to latest version of RAPIDS 0.13 (#81)

* make the code 0.13 compatible * fixed the unit test bugs * fixed portfolio notebook * fixed xgboost node * get rid of the warning * fixed the flake8 bugs * cupy is installed by default * fixed the remaining notebooks * remove the default jupyter-lab entrypoint * activate rapids * added unit test to cover the return feature * added 3 node tests * using the series * fixed the groupby * fixed the port bug
NVIDIA · May 19, 2020 · 273d50f · 273d50f
1 parent 27ae2c9
commit 273d50f
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Showing 35 changed files with 1,022 additions and 1,362 deletions.
diff --git a/docker/build.sh b/docker/build.sh
@@ -32,24 +32,21 @@ echo -e "\nPlease, select your cuda version:\n" \
 
 read -p "Enter your option and hit return [1]-3: " CUDA_VERSION
 
-RAPIDS_VERSION="0.11"
+RAPIDS_VERSION="0.13"
 
 CUDA_VERSION=${CUDA_VERSION:-1}
 case $CUDA_VERSION in
     2)
 	echo "cuda 10.0 selected."
 	CONTAINER_VER='10.0'
-	CUPY='cupy-cuda100'
 	;;
     3)
 	echo "cuda 10.1.2 selected."
 	CONTAINER_VER='10.1'
-	CUPY='cupy-cuda101'
 	;;
     *)
 	echo "cuda 9.2 selected."
 	CONTAINER_VER='9.2'
-	CUPY='cupy-cuda92'
 	;;
 esac
 
@@ -82,8 +79,6 @@ SHELL ["bash","-c"]
 #
 # Additional python libs
 #
-RUN source activate rapids \ 
-    && pip install $CUPY
 
 RUN source activate rapids \
     && cd /rapids/gQuant \
@@ -106,6 +101,7 @@ EXPOSE 8888
 EXPOSE 8787
 EXPOSE 8786
 WORKDIR /rapids
+ENTRYPOINT /bin/bash -c 'source activate rapids; /bin/bash'
 EOF
 
 docker build -f $D_FILE -t $D_CONT .
diff --git a/gquant/cuindicator/ewm.py b/gquant/cuindicator/ewm.py
@@ -49,8 +49,8 @@ def kernel(in_arr, out_arr, average_length, span, arr_len, thread_tile,
         for j in range(0, average_length - 1, block_size):
             if (((tx + j) < average_length - 1) and
                     (starting_id - average_length + 1 + tx + j >= 0)):
-                        shared[tx + j] = \
-                            in_arr[starting_id - average_length + 1 + tx + j]
+                shared[tx + j] = \
+                    in_arr[starting_id - average_length + 1 + tx + j]
         cuda.syncthreads()
         # slice the shared memory for each threads
         start_shared = tx * thread_tile
@@ -95,7 +95,7 @@ def __init__(self, span, input_arr, min_periods=None, thread_tile=48,
         if isinstance(input_arr, numba.cuda.cudadrv.devicearray.DeviceNDArray):
             self.gpu_in = input_arr
         else:
-            self.gpu_in = input_arr.data.to_gpu_array()
+            self.gpu_in = input_arr.to_gpu_array()
         if min_periods is None:
             self.min_periods = span
         else:

diff --git a/gquant/cuindicator/frac_diff.py b/gquant/cuindicator/frac_diff.py
@@ -189,7 +189,7 @@ def fractional_diff(input_arr, d=0.5, floor=1e-3, min_periods=None,
     if isinstance(input_arr, numba.cuda.cudadrv.devicearray.DeviceNDArray):
         gpu_in = input_arr
     else:
-        gpu_in = input_arr.data.to_gpu_array()
+        gpu_in = input_arr.to_gpu_array()
 
     # compute the weights for the fractional difference
     weights = get_weights_floored(d=d,
@@ -269,6 +269,6 @@ def port_fractional_diff(asset_indicator, input_arr, d=0.5, floor=1e-3,
                                    min_periods=min_periods,
                                    thread_tile=thread_tile,
                                    number_of_threads=number_of_threads)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), out, 0,
+    port_mask_nan(asset_indicator.to_gpu_array(), out, 0,
                   len(weights) - 1)
     return out, weights