change output data dir for chainer

Author: Andre Moeller

sagemaker-python-sdk/chainer_cifar10/chainer_single_machine_cifar10.ipynb

@@ -121,7 +121,7 @@
     "* `SM_MODEL_DIR`: A string representing the path to the directory to write model artifacts to.\n",
     "  These artifacts are uploaded to S3 for model hosting.\n",
     "* `SM_NUM_GPUS`: An integer representing the number of GPUs available to the host.\n",
-    "* `SM_OUTPUT_DATA_DIR`: A string representing the filesystem path to write output artifacts to. Output artifacts may\n",
+    "* `SM_OUTPUT_DIR`: A string representing the filesystem path to write output artifacts to. Output artifacts may\n",
     "  include checkpoints, graphs, and other files to save, not including model artifacts. These artifacts are compressed\n",
     "  and uploaded to S3 to the same S3 prefix as the model artifacts.\n",
     "\n",
@@ -291,10 +291,10 @@
     "from IPython.display import Image\n",
     "from IPython.display import display\n",
     "\n",
-    "accuracy_graph = Image(filename=\"output/single_machine_cifar/algo-1/accuracy.png\",\n",
+    "accuracy_graph = Image(filename=\"output/single_machine_cifar/accuracy.png\",\n",
     "                       width=800,\n",
     "                       height=800)\n",
-    "loss_graph = Image(filename=\"output/single_machine_cifar/algo-1/loss.png\",\n",
+    "loss_graph = Image(filename=\"output/single_machine_cifar/loss.png\",\n",
     "                   width=800,\n",
     "                   height=800)\n",
     "\n",
@@ -449,7 +449,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,

sagemaker-python-sdk/chainer_cifar10/chainermn_distributed_cifar10.ipynb

@@ -122,7 +122,7 @@
     "* `SM_MODEL_DIR`: A string representing the path to the directory to write model artifacts to.\n",
     "  These artifacts are uploaded to S3 for model hosting.\n",
     "* `SM_NUM_GPUS`: An integer representing the number of GPUs available to the host.\n",
-    "* `SM_OUTPUT_DATA_DIR`: A string representing the filesystem path to write output artifacts to. Output artifacts may\n",
+    "* `SM_OUTPUT_DIR`: A string representing the filesystem path to write output artifacts to. Output artifacts may\n",
     "  include checkpoints, graphs, and other files to save, not including model artifacts. These artifacts are compressed\n",
     "  and uploaded to S3 to the same S3 prefix as the model artifacts.\n",
     "\n",
@@ -298,10 +298,10 @@
     "from IPython.display import Image\n",
     "from IPython.display import display\n",
     "\n",
-    "accuracy_graph = Image(filename=\"output/distributed_cifar/algo-1/accuracy.png\",\n",
+    "accuracy_graph = Image(filename=\"output/distributed_cifar/accuracy.png\",\n",
     "                       width=800,\n",
     "                       height=800)\n",
-    "loss_graph = Image(filename=\"output/distributed_cifar/algo-1/loss.png\",\n",
+    "loss_graph = Image(filename=\"output/distributed_cifar/loss.png\",\n",
     "                   width=800,\n",
     "                   height=800)\n",
     "\n",
@@ -456,7 +456,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.5"
   },
   "notice": "Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.      amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
  },

sagemaker-python-sdk/chainer_cifar10/src/chainer_cifar_vgg_distributed.py

@@ -43,7 +43,7 @@
     parser.add_argument('--communicator', type=str, default='pure_nccl' if num_gpus > 0 else 'naive')
 
     # Data, model, and output directories. These are required.
-    parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
+    parser.add_argument('--output-dir', type=str, default=os.environ['SM_OUTPUT_DIR'])
     parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
     parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
     parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])
@@ -91,7 +91,8 @@
 
     # Set up a trainer
     updater = training.StandardUpdater(train_iter, optimizer, device=device)
-    trainer = training.Trainer(updater, (args.epochs, 'epoch'), out=args.output_data_dir)
+    output_data_dir = os.path.join(args.output_dir, 'data')
+    trainer = training.Trainer(updater, (args.epochs, 'epoch'), out=output_data_dir)
 
     # Evaluate the model with the test dataset for each epoch
 

sagemaker-python-sdk/chainer_cifar10/src/chainer_cifar_vgg_single_machine.py

@@ -37,7 +37,7 @@
     parser.add_argument('--learning-rate', type=float, default=0.05)
 
     # Data, model, and output directories These are required.
-    parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
+    parser.add_argument('--output-dir', type=str, default=os.environ['SM_OUTPUT_DIR'])
     parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
     parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
     parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])
@@ -82,7 +82,9 @@
         updater = training.updater.StandardUpdater(train_iter, optimizer, device=device)
 
     stop_trigger = (args.epochs, 'epoch')
-    trainer = training.Trainer(updater, stop_trigger, out=args.output_data_dir)
+    
+    output_data_dir = os.path.join(args.output_dir, 'data')
+    trainer = training.Trainer(updater, stop_trigger, out=output_data_dir)
     # Evaluate the model with the test dataset for each epoch
     trainer.extend(extensions.Evaluator(test_iter, model, device=device))
 

sagemaker-python-sdk/chainer_mnist/chainer_mnist_local_mode.ipynb

@@ -137,7 +137,7 @@
     "* `SM_MODEL_DIR`: A string representing the path to the directory to write model artifacts to.\n",
     "  These artifacts are uploaded to S3 for model hosting.\n",
     "* `SM_NUM_GPUS`: An integer representing the number of GPUs available to the host.\n",
-    "* `SM_OUTPUT_DATA_DIR`: A string representing the filesystem path to write output artifacts to. Output artifacts may\n",
+    "* `SM_OUTPUT_DIR`: A string representing the filesystem path to write output artifacts to. Output artifacts may\n",
     "  include checkpoints, graphs, and other files to save, not including model artifacts. These artifacts are compressed\n",
     "  and uploaded to S3 to the same S3 prefix as the model artifacts.\n",
     "\n",
@@ -501,7 +501,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,

sagemaker-python-sdk/chainer_mnist/chainer_mnist_single_machine.py

@@ -49,7 +49,7 @@ def __call__(self, x):
     parser.add_argument('--batch-size', type=int, default=64)
 
     # Data, model, and output directories. These are required.
-    parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
+    parser.add_argument('--output-dir', type=str, default=os.environ['SM_OUTPUT_DIR'])
     parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
     parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
     parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])
@@ -96,7 +96,7 @@ def __call__(self, x):
         updater = training.StandardUpdater(train_iter, optimizer, device=device)
 
     # Write output files to output_data_dir. These are zipped and uploaded to S3 output path as output.tar.gz.
-    trainer = training.Trainer(updater, (args.epochs, 'epoch'), out=args.output_data_dir)
+    trainer = training.Trainer(updater, (args.epochs, 'epoch'), out=args.output_dir)
 
     # Evaluate the model with the test dataset for each epoch
     trainer.extend(extensions.Evaluator(test_iter, model, device=device))

sagemaker-python-sdk/chainer_sentiment_analysis/chainer_sentiment_analysis.ipynb

@@ -124,7 +124,7 @@
     "* `SM_MODEL_DIR`: A string representing the path to the directory to write model artifacts to.\n",
     "  These artifacts are uploaded to S3 for model hosting.\n",
     "* `SM_NUM_GPUS`: An integer representing the number of GPUs available to the host.\n",
-    "* `SM_OUTPUT_DATA_DIR`: A string representing the filesystem path to write output artifacts to. Output artifacts may\n",
+    "* `SM_OUTPUT_DIR`: A string representing the filesystem path to write output artifacts to. Output artifacts may\n",
     "  include checkpoints, graphs, and other files to save, not including model artifacts. These artifacts are compressed\n",
     "  and uploaded to S3 to the same S3 prefix as the model artifacts.\n",
     "\n",
@@ -289,10 +289,10 @@
     "from IPython.display import Image\n",
     "from IPython.display import display\n",
     "\n",
-    "accuracy_graph = Image(filename=\"output/sentiment/algo-1/accuracy.png\",\n",
+    "accuracy_graph = Image(filename=\"output/sentiment/accuracy.png\",\n",
     "                       width=800,\n",
     "                       height=800)\n",
-    "loss_graph = Image(filename=\"output/sentiment/algo-1/loss.png\",\n",
+    "loss_graph = Image(filename=\"output/sentiment/loss.png\",\n",
     "                   width=800,\n",
     "                   height=800)\n",
     "\n",
@@ -409,7 +409,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.5"
   },
   "notice": "Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.      amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
  },

sagemaker-python-sdk/chainer_sentiment_analysis/src/sentiment_analysis.py

@@ -38,7 +38,7 @@
     parser.add_argument('--model-type', type=str, default='rnn')
 
     # Data, model, and output directories. These are required.
-    parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
+    parser.add_argument('--output-dir', type=str, default=os.environ['SM_OUTPUT_DIR'])
     parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
     parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
     parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])
@@ -103,7 +103,11 @@
         test_iter = chainer.iterators.SerialIterator(test, args.batch_size, repeat=False, shuffle=False)
         updater = training.updater.StandardUpdater(train_iter, optimizer, converter=convert_seq, device=device)
 
-    trainer = training.Trainer(updater, (args.epochs, 'epoch'), out=args.output_data_dir)
+    # SageMaker saves the return value of train() in the `save` function in the resulting
+    # model artifact model.tar.gz, and the contents of `output_data_dir` in the output
+    # artifact output.tar.gz.
+    output_data_dir = os.path.join(args.output_dir, 'data')
+    trainer = training.Trainer(updater, (args.epochs, 'epoch'), out=output_data_dir)
 
     # Evaluate the model with the test dataset for each epoch
     trainer.extend(extensions.Evaluator(test_iter, model, converter=convert_seq, device=device))
@@ -138,15 +142,11 @@
     # Run the training
     trainer.run()
 
-    # SageMaker saves the return value of train() in the `save` function in the resulting
-    # model artifact model.tar.gz, and the contents of `output_data_dir` in the output
-    # artifact output.tar.gz.
-
     # load the best model
-    serializers.load_npz(os.path.join(args.output_data_dir, 'best_model.npz'), model)
+    serializers.load_npz(os.path.join(output_data_dir, 'best_model.npz'), model)
 
     # remove the best model from output artifacts (since it will be saved as a model artifact)
-    os.remove(os.path.join(args.output_data_dir, 'best_model.npz'))
+    os.remove(os.path.join(output_data_dir, 'best_model.npz'))
 
     serializers.save_npz(os.path.join(args.model_dir, 'my_model.npz'), model)
     with open(os.path.join(args.model_dir, 'vocab.json'), 'w') as f: