[GitHub] [tvm] driazati commented on a diff in pull request #11557: [Runtime][PipelineExecutor] Tutorial of using pipeline executor.

[GitBox <gi...@apache.org>]

driazati commented on code in PR #11557:
URL: https://github.com/apache/tvm/pull/11557#discussion_r899510232


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gallery/how_to/work_with_relay/using_with_pipeline_executor.py:
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@@ -0,0 +1,182 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License 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.
+"""
+Using Pipeline Executor in Relay
+=================================
+**Author**: `Hua Jiang <https://https://github.com/huajsj>`_
+
+This is a short tutorial on how to use the Pipeline Executor with Relay.
+"""
+import tvm
+from tvm import te
+import numpy as np
+from tvm.contrib import graph_executor as runtime
+from tvm import relay
+from tvm.relay import testing
+import tvm.testing
+import time
+
+#######################################################################
+# Create a simple network, this network can be a pre-trained model too.
+# ---------------------------------------------------------------------
+# Let's create a very simple network for demonstration.
+# It consists of convolution, batch normalization, and ReLU activation.
+def get_network():
+    out_channels = 16
+    batch_size = 1
+    data = relay.var("data", relay.TensorType((batch_size, 3, 224, 224), "float32"))
+    weight = relay.var("weight")
+    second_weight = relay.var("second_weight")
+    bn_gamma = relay.var("bn_gamma")
+    bn_beta = relay.var("bn_beta")
+    bn_mmean = relay.var("bn_mean")
+    bn_mvar = relay.var("bn_var")
+    simple_net = relay.nn.conv2d(
+        data=data, weight=weight, kernel_size=(3, 3), channels=out_channels, padding=(1, 1)
+    )
+    simple_net = relay.nn.batch_norm(simple_net, bn_gamma, bn_beta, bn_mmean, bn_mvar)[0]
+    simple_net = relay.nn.relu(simple_net)
+    simple_net = relay.nn.conv2d(
+        data=simple_net,
+        weight=second_weight,
+        kernel_size=(3, 3),
+        channels=out_channels,
+        padding=(1, 1),
+    )
+    simple_net = relay.Function(relay.analysis.free_vars(simple_net), simple_net)
+    data_shape = (batch_size, 3, 224, 224)
+    net, params = testing.create_workload(simple_net)
+    return net, params, data_shape
+
+
+net, params, data_shape = get_network()
+#############################################
+# Apply a customer graph splitting function.
+# ------------------------------------------
+# We use an testing linear graph splitting function as a example. User also can create their
+# own splitting function logic.
+import os
+
+os.sys.path.append(os.path.abspath(os.environ["TVM_HOME"] + "/tests/python/relay"))

Review Comment:
   We could definitely do that and probably will soon. as a stop gap in the meantime @huajsj you can run the docker build locally and pass it to `ci.py`:
   ```bash
   bash docker/build.sh ci_gpu --tag my_ci_gpu
   python tests/scripts/ci.py docs --docker-image my_ci_gpu
   ```



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