We actually added a specific call to stop the local execution and continue remotely , see it here: https://github.com/allegroai/trains/blob/master/trains/task.py#L2409

and do you have import tensorflow in your code?

and it continues... but nothing about time out

yes

No the "original one"

the one i cloned?

@ https://app.slack.com/team/UT8T0V3NE any idea for what is the reason they are not listed there?

Yes actually that might be it. Here is how it works,
It launch a thread in the background to do all the analysis of the repository, extracting all the packages.
If the process ends (for any reason), it will give the background thread 10 seconds to finish and then it will give up. If the repository is big, the analysis can take longer, and it will quit

i can send requirements.txt if needed

at the end of the manual execution

do you have git repo link in the execution section of the experiment ?

I ran it till the end first time and it ended successfully but i put the trains commend line on comment so i didn't see it on trains,,, so i ran it again and stoped it at the middle...

yes

because it should have detected it...
Did you see "Repository and package analysis timed out ..."

(the Clone)

(with the right branch, commit and all...)

OK I guess you ment the installed packages on the execution tab and i looked at the log.. so there i see the following:

Python 3.7.5 (default, Nov 7 2019, 10:50:52) [GCC 8.3.0]

argparse_utils == 1.3.0
trains == 0.14.3

Detailed import analysis

**************************

IMPORT PACKAGE argparse_utils

simple_pipe2.py: 10

IMPORT PACKAGE trains

simple_pipe2.py: 9

in the clone I see "Environment setup completed successfully" and nothing about timeout

in the "original one"'s log i see:
TRAINS Task: created new task id=2eec56cb60e9441897a4af9c10f656c0
TRAINS new package available: UPGRADE to v0.15.0 is recommended!
Release Notes:
Features

• Add automation support including hyper-parameters optimization (see example here )
• Task.init() auto_connect_arg_parser argument can accept a dictionary disabling specific keys from the argparser (Trains Slack channel thread )
• Allow worker_id override using TRAINS_WORKER_NAME environment variable (Trains Slack channel thread )
• Support layout configuration for plotly objects using extra_layout argument in all Logger reporting methods #136
• Add Task.execute_remotely() to allow cloning and enqueuing a locally executed task (or stopping and re-enqueuing a remotely executed task) #128
• Add Parquet framework and model type
• Support recursive model folder packaging
• Add Task.get_reported_console_output() and Task.get_reported_scalars() to allow retrieval of reported reported output and scalar metrics
• Add Task.add_requirements() to force requirement package into "installed packages"
• Improve task reuse responsiveness
• Add raise_on_error (default False) argument to Model.get_local_copy() and Artifact.get_local_copy() https://github.com/allegroai/trains-agent/issues/17
• Support Task.get_task() without project name (i.e. all projects)
• Support using the file calling Task.init() as the task's script in case sys.argv doesn't point to a git repository
• Support detecting and remotely executing code running from a module (i.e. -m module)
• Add callback for framework save/load binding for better integration with pytorch/ignite https://github.com/pytorch/ignite/issues/1056
• Support new task types provided in Trains Server v0.15.0
• Add automation and distributed examples
• Upgrade default pip version to <20.2
  Bug Fixes
• Fix exact_match_regex() in case of empty pattern #138
• Address deprecation warning and newer attrs versions in MetricsEventAdapter #134
• Fix issues with plotly support (Trains Slack channel thread and thread )
• Fix default argument behavior to match argparse behavior
• Fix OutputModel with task=None should use current task, if exists
• Fix Task.get_task() to raise proper error on incorrect task_id
• Fix Task.enqueue() to use an exact queue name match
• Fix NaN, Inf and -Inf values display in reported table (not supported by JSON)
• Limit max requirement size to 0.5MB
• Fix issues with repository analysis
• Fix StorageManager should only try to extract .zip files, Model should not auto extract package https://github.com/allegroai/trains-agent/issues/17
  TRAINS results page: http://trains.agro-scout.com:8080/projects/071e139ad6084f2493ff00fe9181f825/experiments/2eec56cb60e9441897a4af9c10f656c0/output/log
  inference

It should be the last line (or almost) of the Log. is it there ? Also it seems that from the log, that trains you are using trains 0.14.3 , try with trains 0.15 , let me know if you are still missing packages

"Trains will analyze the entire repository if this is a git repo" it is a git repo...

?

so it should be installed?

if i stopped the running of the Original one before it ended, can it be the problem?

ok, thank you 🙂 i will check it

@ https://app.slack.com/team/UT8T0V3NE ok so I see the packages that are listed are the ones that are imported on the python script that runs but it doesn't include the ones it needs from imports from other inner classes... I guess it is the way it works, so i need to specify all the packages that are not on the py script i run? (all the packages that the inner classes are using)

If the problem consists (i.e. trains failing to detect packages, please open a GitHub Issue so the bug will not get lost 🙂

and keras and many others...

so yea... the packages are not there...

PlainSquid19 Trains will analyze the entire repository if this is a git repo code, and a single script file if there is no repository found.

It will not analyze an entire folder if it is not in a git repository, because it will not be able to recreate this folder anyhow. Make sense ?