fix: update requirements and use the default device in the tutorials;

WenjieDu · Mar 18, 2024 · 831ac32 · 831ac32
1 parent 9028c56
commit 831ac32
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Showing 4 changed files with 22 additions and 39 deletions.
diff --git a/PyPOTS_Classification.ipynb b/PyPOTS_Classification.ipynb
@@ -134,7 +134,7 @@
     "    num_workers=0,\n",
     "    # just leave it to default as None, PyPOTS will automatically assign the best device for you.\n",
     "    # Set it as 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices, even parallelly on ['cuda:0', 'cuda:1']\n",
-    "    device='cuda',  \n",
+    "    device=None,  \n",
     "    # set the path for saving tensorboard and trained model files \n",
     "    saving_path=\"tutorial_results/classification/raindrop\",\n",
     "    model_saving_strategy=\"best\", # only save the best model after training finished. \n",
@@ -196,8 +196,7 @@
       "PR_AUC: 0.4184412610743794,\n",
       "F1: 0.41390205371248023,\n",
       "Precision: 0.447098976109215,\n",
-      "Recall: 0.38529411764705884,\n",
-      "\n"
+      "Recall: 0.38529411764705884,\n"
      ]
     }
    ],
@@ -263,7 +262,7 @@
     "    num_workers=0,\n",
     "    # just leave it to default as None, PyPOTS will automatically assign the best device for you.\n",
     "    # Set it as 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices, even parallelly on ['cuda:0', 'cuda:1']\n",
-    "    device='cuda',  \n",
+    "    device=None,  \n",
     "    # set the path for saving tensorboard and trained model files \n",
     "    saving_path=\"tutorial_results/classification/brits\",\n",
     "    # only save the best model after training finished.\n",
@@ -326,8 +325,7 @@
       "PR_AUC: 0.40568596808086554,\n",
       "F1: 0.3706293706293706,\n",
       "Precision: 0.45689655172413796,\n",
-      "Recall: 0.31176470588235294,\n",
-      "\n"
+      "Recall: 0.31176470588235294,\n"
      ]
     }
    ],
@@ -393,7 +391,7 @@
     "    num_workers=0,\n",
     "    # just leave it to default as None, PyPOTS will automatically assign the best device for you.\n",
     "    # Set it as 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices, even parallelly on ['cuda:0', 'cuda:1']\n",
-    "    device='cuda',  \n",
+    "    device=None,  \n",
     "    # set the path for saving tensorboard and trained model files \n",
     "    saving_path=\"tutorial_results/classification/grud\",\n",
     "    # only save the best model after training finished.\n",
@@ -455,8 +453,7 @@
       "PR_AUC: 0.48160561802559804,\n",
       "F1: 0.41064638783269963,\n",
       "Precision: 0.5806451612903226,\n",
-      "Recall: 0.3176470588235294,\n",
-      "\n"
+      "Recall: 0.3176470588235294,\n"
      ]
     }
    ],

diff --git a/PyPOTS_Clustering.ipynb b/PyPOTS_Clustering.ipynb
@@ -131,7 +131,7 @@
     "    num_workers=0,\n",
     "    # just leave it to default as None, PyPOTS will automatically assign the best device for you.\n",
     "    # Set it as 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices, even parallelly on ['cuda:0', 'cuda:1']\n",
-    "    device='cuda',  \n",
+    "    device=None,  \n",
     "    # set the path for saving tensorboard and trained model files \n",
     "    saving_path=\"tutorial_results/clustering/crli\",\n",
     "    # only save the best model after training finished.\n",
@@ -186,8 +186,7 @@
      "text": [
       "Testing clustering metrics: \n",
       "RI: 0.6352735191995277, \n",
-      "CP: 0.8582151793160967\n",
-      "\n"
+      "CP: 0.8582151793160967\n"
      ]
     }
    ],
@@ -254,7 +253,7 @@
     "    num_workers=0,\n",
     "    # just leave it to default as None, PyPOTS will automatically assign the best device for you.\n",
     "    # Set it as 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices, even parallelly on ['cuda:0', 'cuda:1']\n",
-    "    device='cuda',  \n",
+    "    device=None,  \n",
     "    # set the path for saving tensorboard and trained model files \n",
     "    saving_path=\"tutorial_results/clustering/vader\",\n",
     "    # only save the best model after training finished.\n",
@@ -314,8 +313,7 @@
      "text": [
       "Testing clustering metrics: \n",
       "RI: 0.7565347009032349, \n",
-      "CP: 0.8582151793160967,\n",
-      "\n"
+      "CP: 0.8582151793160967,\n"
      ]
     }
    ],

diff --git a/PyPOTS_Imputation.ipynb b/PyPOTS_Imputation.ipynb
@@ -136,7 +136,7 @@
     "    num_workers=0,\n",
     "    # just leave it to default as None, PyPOTS will automatically assign the best device for you.\n",
     "    # Set it as 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices, even parallelly on ['cuda:0', 'cuda:1']\n",
-    "    device='cuda',  \n",
+    "    device=None,  \n",
     "    # set the path for saving tensorboard and trained model files \n",
     "    saving_path=\"tutorial_results/imputation/saits\",\n",
     "    # only save the best model after training finished.\n",
@@ -269,7 +269,7 @@
     "    num_workers=0,\n",
     "    # just leave it to default as None, PyPOTS will automatically assign the best device for you.\n",
     "    # Set it as 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices, even parallelly on ['cuda:0', 'cuda:1']\n",
-    "    device='cuda',  \n",
+    "    device=None,  \n",
     "    # set the path for saving tensorboard and trained model files \n",
     "    saving_path=\"tutorial_results/imputation/transformer\",\n",
     "    # only save the best model after training finished.\n",
@@ -399,7 +399,7 @@
     "    num_workers=0,\n",
     "    # just leave it to default as None, PyPOTS will automatically assign the best device for you.\n",
     "    # Set it as 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices, even parallelly on ['cuda:0', 'cuda:1']\n",
-    "    device='cuda',  \n",
+    "    device=None,  \n",
     "    # set the path for saving tensorboard and trained model files \n",
     "    saving_path=\"tutorial_results/imputation/timesnet\",\n",
     "    # only save the best model after training finished.\n",
@@ -675,7 +675,7 @@
     "    num_workers=0,\n",
     "    # just leave it to default as None, PyPOTS will automatically assign the best device for you.\n",
     "    # Set it as 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices, even parallelly on ['cuda:0', 'cuda:1']\n",
-    "    device='cuda',\n",
+    "    device=None,\n",
     "    # set the path for saving tensorboard and trained model files \n",
     "    saving_path=\"tutorial_results/imputation/us_gan\",\n",
     "    # only save the best model after training finished.\n",
@@ -809,7 +809,7 @@
     "    num_workers=0,\n",
     "    # just leave it to default as None, PyPOTS will automatically assign the best device for you.\n",
     "    # Set it as 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices, even parallelly on ['cuda:0', 'cuda:1']\n",
-    "    device='cuda',  \n",
+    "    device=None,  \n",
     "    # set the path for saving tensorboard and trained model files \n",
     "    saving_path=\"tutorial_results/imputation/gp_vae\",\n",
     "    # only save the best model after training finished.\n",
@@ -945,7 +945,7 @@
     "    num_workers=0,\n",
     "    # just leave it to default as None, PyPOTS will automatically assign the best device for you.\n",
     "    # Set it as 'cpu' if you don't have CUDA devices. You can also set it to 'cuda:0' or 'cuda:1' if you have multiple CUDA devices, even parallelly on ['cuda:0', 'cuda:1']\n",
-    "    device='cuda',\n",
+    "    device=None,\n",
     "    # set the path for saving tensorboard and trained model files \n",
     "    saving_path=\"tutorial_results/imputation/brits\", \n",
     "    # only save the best model after training finished.\n",
@@ -1030,10 +1030,7 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [
     {
@@ -1086,10 +1083,7 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [
     {
@@ -1119,10 +1113,7 @@
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -1135,10 +1126,7 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
    },
    "outputs": [
     {

diff --git a/conda-environment.yml b/conda-environment.yml
@@ -19,9 +19,9 @@ dependencies:
     - conda-forge::tsdb
     - conda-forge::protobuf <=4.21.12
     - pytorch::pytorch >=1.10.0
-    - conda-forge::pypots >=0.1.1
+    - conda-forge::pypots >=0.3.1
     - conda-forge::tsdb
-    - conda-forge::pycorruptor
+    - conda-forge::pygrinder
     - conda-forge::jupyterlab
 
     # optional