Sure @<1523701087100473344:profile|SuccessfulKoala55> ! Here it is!
Right, but there is a lot of free space (257 GB) in the home folder
any idea what could be the issue @<1523701087100473344:profile|SuccessfulKoala55> ?
can you share your clearml.conf file (remove the critical information first)?
None
This last solution @<1523701205467926528:profile|AgitatedDove14> proposed did the trick!
agree, but setting the agent’s env variable TMPDIR
I think this needs to be passed to the docker with -e TMPDIR=/new/tmp as additional container args:
see example
None
wdyt?
# ClearML SDK configuration file
api {
# Notice: 'host' is the api server (default port 8008), not the web server.
api_server:
web_server:
files_server:
# Credentials are generated using the webapp,
# Override with os environment: CLEARML_API_ACCESS_KEY / CLEARML_API_SECRET_KEY
credentials {"access_key": REMOVED, "secret_key": REMOVED}
}
sdk {
# ClearML - default SDK configuration
storage {
cache {
# Defaults to system temp folder / cache
default_base_dir: "~/.clearml/cache"
# default_cache_manager_size: 100
}
direct_access: [
# Objects matching are considered to be available for direct access, i.e. they will not be downloaded
# or cached, and any download request will return a direct reference.
# Objects are specified in glob format, available for url and content_type.
{ url: "file://*" } # file-urls are always directly referenced
]
}
metrics {
# History size for debug files per metric/variant. For each metric/variant combination with an attached file
# (e.g. debug image event), file names for the uploaded files will be recycled in such a way that no more than
# X files are stored in the upload destination for each metric/variant combination.
file_history_size: 100
# Max history size for matplotlib imshow files per plot title.
# File names for the uploaded images will be recycled in such a way that no more than
# X images are stored in the upload destination for each matplotlib plot title.
matplotlib_untitled_history_size: 100
# Limit the number of digits after the dot in plot reporting (reducing plot report size)
# plot_max_num_digits: 5
# Settings for generated debug images
images {
format: JPEG
quality: 87
subsampling: 0
}
# Support plot-per-graph fully matching Tensorboard behavior (i.e. if this is set to true, each series should have its own graph)
tensorboard_single_series_per_graph: false
}
network {
metrics {
# Number of threads allocated to uploading files (typically debug images) when transmitting metrics for
# a specific iteration
file_upload_threads: 4
# Warn about upload starvation if no uploads were made in specified period while file-bearing events keep
# being sent for upload
file_upload_starvation_warning_sec: 120
}
iteration {
# Max number of retries when getting frames if the server returned an error (http code 500)
max_retries_on_server_error: 5
# Backoff factory for consecutive retry attempts.
# SDK will wait for {backoff factor} * (2 ^ ({number of total retries} - 1)) between retries.
retry_backoff_factor_sec: 10
}
}
aws {
s3 {
# S3 credentials, used for read/write access by various SDK elements
# Default, used for any bucket not specified below
region: ""
# Specify explicit keys
key: ""
secret: ""
# Or enable credentials chain to let Boto3 pick the right credentials.
# This includes picking credentials from environment variables,
# credential file and IAM role using metadata service.
# Refer to the latest Boto3 docs
use_credentials_chain: false
# Additional ExtraArgs passed to boto3 when uploading files. Can also be set per-bucket under "credentials".
extra_args: {}
credentials: [
# specifies key/secret credentials to use when handling s3 urls (read or write)
# {
# bucket: "my-bucket-name"
# key: "my-access-key"
# secret: "my-secret-key"
# },
# {
# # This will apply to all buckets in this host (unless key/value is specifically provided for a given bucket)
# host: "my-minio-host:9000"
# key: "12345678"
# secret: "12345678"
# multipart: false
# secure: false
# }
]
}
boto3 {
pool_connections: 512
max_multipart_concurrency: 16
}
}
google.storage {
# # Default project and credentials file
# # Will be used when no bucket configuration is found
# project: "clearml"
# credentials_json: "/path/to/credentials.json"
# pool_connections: 512
# pool_maxsize: 1024
# # Specific credentials per bucket and sub directory
# credentials = [
# {
# bucket: "my-bucket"
# subdir: "path/in/bucket" # Not required
# project: "clearml"
# credentials_json: "/path/to/credentials.json"
# },
# ]
}
azure.storage {
# containers: [
# {
# account_name: "clearml"
# account_key: "secret"
# # container_name:
# }
# ]
}
log {
# debugging feature: set this to true to make null log propagate messages to root logger (so they appear in stdout)
null_log_propagate: false
task_log_buffer_capacity: 66
# disable urllib info and lower levels
disable_urllib3_info: true
}
development {
# Development-mode options
# dev task reuse window
task_reuse_time_window_in_hours: 72.0
# Run VCS repository detection asynchronously
vcs_repo_detect_async: true
# Store uncommitted git/hg source code diff in experiment manifest when training in development mode
# This stores "git diff" or "hg diff" into the experiment's "script.requirements.diff" section
store_uncommitted_code_diff: true
# Support stopping an experiment in case it was externally stopped, status was changed or task was reset
support_stopping: true
# Default Task output_uri. if output_uri is not provided to Task.init, default_output_uri will be used instead.
default_output_uri: ""
# Default auto generated requirements optimize for smaller requirements
# If True, analyze the entire repository regardless of the entry point.
# If False, first analyze the entry point script, if it does not contain other to local files,
# do not analyze the entire repository.
force_analyze_entire_repo: false
# If set to true, *clearml* update message will not be printed to the console
# this value can be overwritten with os environment variable CLEARML_SUPPRESS_UPDATE_MESSAGE=1
suppress_update_message: false
# If this flag is true (default is false), instead of analyzing the code with Pigar, analyze with `pip freeze`
detect_with_pip_freeze: false
# Log specific environment variables. OS environments are listed in the "Environment" section
# of the Hyper-Parameters.
# multiple selected variables are supported including the suffix '*'.
# For example: "AWS_*" will log any OS environment variable starting with 'AWS_'.
# This value can be overwritten with os environment variable CLEARML_LOG_ENVIRONMENT="[AWS_*, CUDA_VERSION]"
# Example: log_os_environments: ["AWS_*", "CUDA_VERSION"]
log_os_environments: []
# Development mode worker
worker {
# Status report period in seconds
report_period_sec: 2
# ping to the server - check connectivity
ping_period_sec: 30
# Log all stdout & stderr
log_stdout: true
# Carriage return (\r) support. If zero (0) \r treated as \n and flushed to backend
# Carriage return flush support in seconds, flush consecutive line feeds (\r) every X (default: 10) seconds
console_cr_flush_period: 10
# compatibility feature, report memory usage for the entire machine
# default (false), report only on the running process and its sub-processes
report_global_mem_used: false
}
}
# Apply top-level environment section from configuration into os.environ
apply_environment: false
# Top-level environment section is in the form of:
# environment {
# key: value
# ...
# }
# and is applied to the OS environment as `key=value` for each key/value pair
# Apply top-level files section from configuration into local file system
apply_files: false
# Top-level files section allows auto-generating files at designated paths with a predefined contents
# and target format. Options include:
# contents: the target file's content, typically a string (or any base type int/float/list/dict etc.)
# format: a custom format for the contents. Currently supported value is `base64` to automatically decode a
# base64-encoded contents string, otherwise ignored
# path: the target file's path, may include ~ and inplace env vars
# target_format: format used to encode contents before writing into the target file. Supported values are json,
# yaml, yml and bytes (in which case the file will be written in binary mode). Default is text mode.
# overwrite: overwrite the target file in case it exists. Default is true.
#
# Example:
# files {
# myfile1 {
# contents: "The quick brown fox jumped over the lazy dog"
# path: "/tmp/fox.txt"
# }
# myjsonfile {
# contents: {
# some {
# nested {
# value: [1, 2, 3, 4]
# }
# }
# }
# path: "/tmp/test.json"
# target_format: json
# }
# }
}
oh ok, I was wondering if this could have been an issue:agent.venvs_cache.free_space_threshold_gb = 2.0
I had tried adding those environment variables, but not in the agents 🙈
Hi @<1523701087100473344:profile|SuccessfulKoala55> . I'm trying to run an optimization task, based on a previous experiment. I ran the agent like this:
clearml-agent daemon --queue my_queue -d
@<1523703080200179712:profile|NastySeahorse61> / @<1523702868694011904:profile|AbruptCow41>
Is there a way to avoid each task to create a new environment?
You can just define CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1 it will just use whatever you have there (notice it will totally ignore requirements.txt and "installed packages" on the Task)
BTW I would recommend turning on the venv caching, this is per docker/python/packages caching so the next time you are using th exact requirements it just pulls it from the cache and attaches to the container.
Un-comment this line
None
line 120 says unmark to enable venv caching (it comes commented by default, but since I’m copying my conf it isn’t commented there)
The specific problem I have is this error message:
ERROR: Could not install packages due to an EnvironmentError: [Errno 28] No space left on device
clearml_agent: ERROR: Could not install task requirements!
But I'm sure I have space... I wanted to use a predefined virtualenv as a walkaround, but maybe I can fix this original problem
Thanks so much for all your help @<1523701205467926528:profile|AgitatedDove14> @<1523702868694011904:profile|AbruptCow41> @<1523701087100473344:profile|SuccessfulKoala55>
also I suggested to change TMPDIR env variable, since /tmp/ didn’t have a lot of space.
agent.environment.TMPDIR = ****
is it ok to see * ** * instead of the actual path?
ERROR: Could not install packages due to an EnvironmentError:
[Errno 28] No space left on device
BTW: @<1523703080200179712:profile|NastySeahorse61> this sounds like docker out of space on the Main disk '/var/` where it stores all the images and temp file systems
This will cause you code to fail as any runtime change to the container file system will raise this out of disk space error
I agree, but setting the agent’s env variable TMPDIR didn’t seem to have any effect (check the log above, it is still using /tmp )
and then run my script from terminal normally... (in the case of the environment variable I passed it before the python command)
oh sorry my bad, then you probably need to define all OS environment variable for python temp folder for the agent (the Task process itself is a child process so it will inherit it)
TMPDIR/new/tmp TMP=/new/tmp TEMP=/new/tmp clearml-agent daemon ...
Can you share the agent's/task full log when running this task?
To give more context, he is running an hyper params optimization script, that internally clones a base task and runs it with certain params and checks if a metric increases or decreases. It is when the agent tries to run this task that the error raises.
ERROR: Could not install packages due to an EnvironmentError: [Errno 28] No space left on device
clearml_agent: ERROR: Could not install task requirements!
Command '['~/.clearml/venvs-builds/3.8/bin/python', '-m', 'pip', '--disable-pip-version-check', 'install', '-r', '/tmp/cached-reqsot4de9w6.txt']' returned non-zero exit status 1.
Is it possible that the agent is somehow limiting the space for the environment creation @<1523701087100473344:profile|SuccessfulKoala55> ?
Because if he runs the same command in a console the install works
Well, the agent actually can't limits this space even if we wanted to 🙂
Then the only other option is the /tmp is out of space (pip uses it to uncompress the .whl files, then it deletes them)
wdyt?
Hi Lema Gabriel, thank you very much for your answer. I'm just using the defaults... Should I change something in the configuration?
I'll attach my config just in case