MLIA-551 Rework remains of AIET architecture

Re-factoring the code base to further merge the old AIET code into MLIA.

- Remove last traces of the backend type 'tool'
- Controlled systems removed, including SSH protocol, controller,
  RunningCommand, locks etc.
- Build command / build dir and deploy functionality removed from
  Applications and Systems
- Moving working_dir()
- Replace module 'output_parser' with new module 'output_consumer' and
  merge Base64 parsing into it
- Change the output consumption to optionally remove (i.e. actually
  consume) lines
- Use Base64 parsing in GenericInferenceOutputParser, replacing the
  regex-based parsing and remove the now unused regex parsing
- Remove AIET reporting
- Pre-install applications by moving them to src/mlia/resources/backends
- Rename aiet-config.json to backend-config.json
- Move tests from tests/mlia/ to tests/
- Adapt unit tests to code changes
- Dependencies removed: paramiko, filelock, psutil
- Fix bug in corstone.py: The wrong resource directory was used which
  broke the functionality to download backends.
- Use f-string formatting.
- Use logging instead of print.

Change-Id: I768bc3bb6b2eda57d219ad01be4a8e0a74167d76

1 files changed, 0 insertions, 32 deletions

diff --git a/tests/mlia/utils/common.py b/tests/mlia/utils/common.py
deleted file mode 100644
index 932343e..0000000
--- a/tests/mlia/utils/common.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
-# SPDX-License-Identifier: Apache-2.0
-"""Common test utils module."""
-from typing import Tuple
-
-import numpy as np
-import tensorflow as tf
-
-
-def get_dataset() -> Tuple[np.ndarray, np.ndarray]:
-    """Return sample dataset."""
-    mnist = tf.keras.datasets.mnist
-    (x_train, y_train), _ = mnist.load_data()
-    x_train = x_train / 255.0
-
-    # Use subset of 60000 examples to keep unit test speed fast.
-    x_train = x_train[0:1]
-    y_train = y_train[0:1]
-
-    return x_train, y_train
-
-
-def train_model(model: tf.keras.Model) -> None:
-    """Train model using sample dataset."""
-    num_epochs = 1
-
-    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-    model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
-
-    x_train, y_train = get_dataset()
-
-    model.fit(x_train, y_train, epochs=num_epochs)