Why Unity's ML-Agents are not working with Google Colab - unity3d

Question

I am trying to train ML-Agents on Google colab but every time it fails with the same given error.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/compat/v2_compat.py:65: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
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Version information:
ml-agents: 0.15.1,
ml-agents-envs: 0.15.1,
Communicator API: 0.15.0,
TensorFlow: 2.0.2
Found path: /content/build/test/ball.x86_64
Mono path[0] = '/content/build/test/ball_Data/Managed'
Mono config path = '/content/build/test/ball_Data/MonoBleedingEdge/etc'
Preloaded 'lib_burst_generated.so'
Preloaded 'libgrpc_csharp_ext.x64.so'
Initialize engine version: 2018.4.21f1 (fd3915227633)
Forcing GfxDevice: Null
GfxDevice: creating device client; threaded=0
NullGfxDevice:
Version: NULL 1.0 [1.0]
Renderer: Null Device
Vendor: Unity Technologies
Begin MonoManager ReloadAssembly
- Completed reload, in 0.147 seconds
WARNING: Shader Unsupported: 'Autodesk Interactive' - Pass 'FORWARD' has no vertex shader
WARNING: Shader Unsupported: 'Autodesk Interactive' - Pass 'FORWARD_DELTA' has no vertex shader
WARNING: Shader Unsupported: 'Autodesk Interactive' - Pass 'ShadowCaster' has no vertex shader
WARNING: Shader Unsupported: 'Autodesk Interactive' - All passes removed
UnloadTime: 1.492000 ms
Fallback handler could not load library /content/build/test/ball_Data/Mono/libcoreclr.so
Fallback handler could not load library /content/build/test/ball_Data/Mono/libcoreclr.so
Fallback handler could not load library /content/build/test/ball_Data/Mono/libcoreclr.so
Fallback handler could not load library /content/build/test/ball_Data/Mono/libSystem.dylib
Fallback handler could not load library /content/build/test/ball_Data/Mono/libSystem.dylib.so
Fallback handler could not load library /content/build/test/ball_Data/Mono/libSystem.dylib
Fallback handler could not load library /content/build/test/ball_Data/Mono/libcoreclr.so
Fallback handler could not load library /content/build/test/ball_Data/Mono/libcoreclr.so
Fallback handler could not load library /content/build/test/ball_Data/Mono/libcoreclr.so
Fallback handler could not load library /content/build/test/ball_Data/Mono/libSystem.dylib
Fallback handler could not load library /content/build/test/ball_Data/Mono/libSystem.dylib.so
Fallback handler could not load library /content/build/test/ball_Data/Mono/libSystem.dylib
2020-05-29 13:53:12 INFO [environment.py:160] Connected to Unity environment with package version 0.15.1-preview and communication version 0.15.0
2020-05-29 13:53:12 INFO [environment.py:305] Connected new brain:
3DBall?team=0
2020-05-29 13:53:12 INFO [trainer_controller.py:167] Hyperparameters for the PPOTrainer of brain 3DBall:
trainer: ppo
... (Hyperparameter list)
summary_path: test-1_3DBall
model_path: ./models/test-1/3DBall
keep_checkpoints: 5
2020-05-29 13:53:12.718558: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-05-29 13:53:12.732085: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz
2020-05-29 13:53:12.732627: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1818f40 executing computations on platform Host. Devices:
2020-05-29 13:53:12.732677: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Host, Default Version
Receiving unhandled NULL exception
2020-05-29 13:54:17 INFO [subprocess_env_manager.py:150] UnityEnvironment worker 0: environment stopping.
2020-05-29 13:54:17 INFO [trainer_controller.py:104] Learning was interrupted. Please wait while the graph is generated.
2020-05-29 13:54:17 INFO [environment.py:455] Environment shut down with return code -11 (SIGSEGV).
2020-05-29 13:54:18 INFO [trainer_controller.py:100] Saved Model
2020-05-29 13:54:18 INFO [model_serialization.py:222] List of nodes to export for brain :3DBall?team=0
2020-05-29 13:54:18 INFO [model_serialization.py:224] is_continuous_control
2020-05-29 13:54:18 INFO [model_serialization.py:224] version_number
2020-05-29 13:54:18 INFO [model_serialization.py:224] memory_size
2020-05-29 13:54:18 INFO [model_serialization.py:224] action_output_shape
2020-05-29 13:54:18 INFO [model_serialization.py:224] action
2020-05-29 13:54:18 INFO [model_serialization.py:224] action_probs
Converting ./models/test-1/3DBall/frozen_graph_def.pb to ./models/test-1/3DBall.nn
IGNORED: Cast unknown layer
IGNORED: Shape unknown layer
IGNORED: StopGradient unknown layer
GLOBALS: 'is_continuous_control', 'version_number', 'memory_size', 'action_output_shape'
IN: 'vector_observation': [-1, 1, 1, 8] => 'sub_2'
OUT: 'action', 'action_probs'
DONE: wrote ./models/test-1/3DBall.nn file.
2020-05-29 13:54:18 INFO [model_serialization.py:76] Exported ./models/test-1/3DBall.nn file
ML-Agents version = 0.15.1 check the repo
Unity Version = 2018.4.21f1 (Used to create a Linux build)
Steps used:
Create a build file for Linux from Unity Editor.
Zipped the build file and uploaded it to google colab.
Unzipped in google colab and enabled the execution permissions.
Cloned and installed the ml-agents repo. same as above repo
Using this command to start the training process
ENV_BINARY = '/content/build/test/ball.x86_64'
mlagents-learn /content/config.yaml --run-id=test-1 --env=$ENV_BINARY --train --no-graphics
Another method which I followed:
Install unity in google colab to create a build file in colab itself but the build didn't work in unity.
To implement this I used notebook
It seems that there are no resources on the internet related to this topic.
So please help me to get this working, I have already wasted my two days in search and trying to run ML-Agents in google colab.
Updates:
The colab notebook started working after I updated my environment build using the Unity engine's 2019.3.15f1 version.

Answer 1

I followed these links and it worked for me, so anyone who is looking for complete implementation can take a look at these:
GitHub repo: ML Agents with Google Colab
Medium article: Training ML-Agents with Google Colab