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clearml initial version 0.17.0

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# `clearml-task` - Execute ANY python code on a remote machine
If you are already familiar with `clearml`, then you can think of `clearml-task` as a way to create a Task/experiment
from any script without the need to add even a single line of code to the original codebase.
`clearml-task` allows a user to **take any python code/repository and launch it on a remote machine**.
The remote execution is fully monitored, all outputs - including console / tensorboard / matplotlib
are logged in real-time into the ClearML UI
## What does it do?
`clearml-task` creates a new experiment on your `clearml-server`; it populates the experiment's environment with:
* repository/commit/branch, as specified by the command-line invocation.
* optional: the base docker image to be used as underlying environment
* optional: alternative python requirements, in case `requirements.txt` is not found inside the repository.
Once the new experiment is created and populated, it will enqueue the experiment to the selected execution queue.
When the experiment is executed on the remote machine (performed by an available `clearml-agent`), all the console outputs
will be logged in real-time, alongside your TensorBoard and matplotlib.
### Use-cases for `clearml-task` remote execution
- You have an off-the-shelf code, and you want to launch it on a remote machine with a specific resource (i.e., GPU)
- You want to run the [hyper-parameter optimization]() on a codebase that is still not connected with `clearml`
- You want to create a [pipeline]() from an assortment of scripts, and you need to create Tasks for those scripts
- Sometimes, you just want to run some code on a remote machine, either using an on-prem cluster or on the cloud...
### Prerequisites
- A single python script, or an up-to-date repository containing the codebase.
- `clearml-agent` running on at least one machine (to execute the experiment)
## Tutorial
### Launching a job from a repository
We will be launching this [script](https://github.com/allegroai/trains/blob/master/examples/frameworks/scikit-learn/sklearn_matplotlib_example.py) on a remote machine. The following are the command-line options we will be using:
- First, we have to give the experiment a name and select a project (`--project examples --name remote_test`)
- Then, we select the repository with our code. If we do not specify branch / commit, it will take the latest commit
from the master branch (`--repo https://github.com/allegroai/clearml.git`)
- Lastly, we need to specify which script in the repository needs to be run (`--script examples/frameworks/scikit-learn/sklearn_matplotlib_example.py`)
Notice that by default, the execution working directory will be the root of the repository. If we need to change it, add `--cwd <folder>`
If we additionally need to pass an argument to our scripts, use the `--args` switch.
The names of the arguments should match the argparse arguments, removing the '--' prefix
(e.g. instead of --key=value -> use `--args key=value` )
``` bash
clearml-task --project examples --name remote_test --repo https://github.com/allegroai/clearml.git
--script examples/frameworks/scikit-learn/sklearn_matplotlib_example.py
--queue single_gpu
```
### Launching a job from a local script
We will be launching a single local script file (no git repo needed) on a remote machine.
- First, we have to give the experiment a name and select a project (`--project examples --name remote_test`)
- Then, we select the script file on our machine, `--script /path/to/my/script.py`
- If we need specific packages, we can specify them manually with `--packages "tqdm>=4" "torch>1.0"`
or we can pass a requirements file `--requirements /path/to/my/requirements.txt`
- Same as in the repo case, if we need to pass arguments to `argparse` we can add `--args key=value`
- If we have a docker container with an entire environment we want our script to run inside,
add e.g., `--docker nvcr.io/nvidia/pytorch:20.11-py3`
Note: In this example, the exact version of PyTorch to install will be resolved by the `clearml-agent` depending on the CUDA environment available at runtime.
``` bash
clearml-task --project examples --name remote_test --script /path/to/my/script.py
--packages "tqdm>=4" "torch>1.0" --args verbose=true
--queue dual_gpu
```
### CLI options
``` bash
clearml-task --help
```
``` console
ClearML launch - launch any codebase on remote machines running clearml-agent
optional arguments:
-h, --help show this help message and exit
--version Display the Allegro.ai utility version
--project PROJECT Required: set the project name for the task. If
--base-task-id is used, this arguments is optional.
--name NAME Required: select a name for the remote task
--repo REPO remote URL for the repository to use. Example: --repo
https://github.com/allegroai/clearml.git
--branch BRANCH Select specific repository branch/tag (implies the
latest commit from the branch)
--commit COMMIT Select specific commit id to use (default: latest
commit, or when used with local repository matching
the local commit id)
--folder FOLDER Remotely execute the code in the local folder. Notice!
It assumes a git repository already exists. Current
state of the repo (commit id and uncommitted changes)
is logged and will be replicated on the remote machine
--script SCRIPT Specify the entry point script for the remote
execution. When used in tandem with --repo the script
should be a relative path inside the repository, for
example: --script source/train.py .When used with
--folder it supports a direct path to a file inside
the local repository itself, for example: --script
~/project/source/train.py
--cwd CWD Working directory to launch the script from. Default:
repository root folder. Relative to repo root or local
folder
--args [ARGS [ARGS ...]]
Arguments to pass to the remote execution, list of
<argument>=<value> strings.Currently only argparse
arguments are supported. Example: --args lr=0.003
batch_size=64
--queue QUEUE Select the queue to launch the task. If not provided a
Task will be created but it will not be launched.
--requirements REQUIREMENTS
Specify requirements.txt file to install when setting
the session. If not provided, the requirements.txt
from the repository will be used.
--packages [PACKAGES [PACKAGES ...]]
Manually specify a list of required packages. Example:
--packages "tqdm>=2.1" "scikit-learn"
--docker DOCKER Select the docker image to use in the remote session
--skip-task-init If set, Task.init() call is not added to the entry
point, and is assumed to be called in within the
script. Default: add Task.init() call entry point
script
--base-task-id BASE_TASK_ID
Use a pre-existing task in the system, instead of a local repo/script.
Essentially clones an existing task and overrides arguments/requirements.
```

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# ClearML SDK configuration file
api {
# web_server on port 8080
web_server: "http://localhost:8080"
# Notice: 'api_server' is the api server (default port 8008), not the web server.
api_server: "http://localhost:8008"
# file server on port 8081
files_server: "http://localhost:8081"
# Credentials are generated using the webapp, http://localhost:8080/profile
credentials {"access_key": "EGRTCO8JMSIGI6S39GTP43NFWXDQOW", "secret_key": "x!XTov_G-#vspE*Y(h$Anm&DIc5Ou-F)jsl$PdOyj5wG1&E!Z8"}
# verify host ssl certificate, set to False only if you have a very good reason
verify_certificate: True
}
sdk {
# ClearML - default SDK configuration
storage {
cache {
# Defaults to system temp folder / cache
default_base_dir: "~/.clearml/cache"
}
}
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
key: ""
secret: ""
region: ""
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"
# # 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
store_code_diff_from_remote: false
# 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
detect_with_conda_freeze: false
# Log specific environment variables. OS environments are enlisted 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
# 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
}
}
}

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Firstly, we thank you for taking the time to contribute!
The following is a set of guidelines for contributing to TRAINS.
Contribution comes in many forms:
* Reporting [issues](https://github.com/allegroai/clearml/issues) you've come upon
* Participating in issue discussions in the [issue tracker](https://github.com/allegroai/clearml/issues) and the [ClearML community slack space](https://join.slack.com/t/allegroai-trains/shared_invite/enQtOTQyMTI1MzQxMzE4LTY5NTUxOTY1NmQ1MzQ5MjRhMGRhZmM4ODE5NTNjMTg2NTBlZGQzZGVkMWU3ZDg1MGE1MjQxNDEzMWU2NmVjZmY)
* Suggesting new features or enhancements
* Implementing new features or fixing outstanding issues
The following is a set of guidelines for contributing to ClearML.
These are primarily guidelines, not rules.
Use your best judgment and feel free to propose changes to this document in a pull request.
## Reporting Bugs
## Reporting Issues
This section guides you through submitting a bug report for TRAINS.
By following these guidelines, you
help maintainers and the community understand your report, reproduce the behavior, and find related reports.
By following these guidelines, you help maintainers and the community understand your report, reproduce the behavior, and find related reports.
Before creating bug reports, please check whether the bug you want to report already appears [here](link to issues).
You may discover that you do not need to create a bug report.
When you are creating a bug report, please include as much detail as possible.
Before reporting an issue, please check whether it already appears [here](https://github.com/allegroai/clearml/issues).
If it does, join the on-going discussion instead.
**Note**: If you find a **Closed** issue that may be the same issue which you are currently experiencing,
then open a **New** issue and include a link to the original (Closed) issue in the body of your new one.
Explain the problem and include additional details to help maintainers reproduce the problem:
When reporting an issue, please include as much detail as possible: explain the problem and include additional details to help maintainers reproduce the problem:
* **Use a clear and descriptive title** for the issue to identify the problem.
* **Describe the exact steps necessary to reproduce the problem** in as much detail as possible. Please do not just summarize what you did. Make sure to explain how you did it.
* **Provide the specific environment setup.** Include the `pip freeze` output, specific environment variables, Python version, and other relevant information.
* **Provide specific examples to demonstrate the steps.** Include links to files or GitHub projects, or copy/paste snippets which you use in those examples.
* **If you are reporting any TRAINS crash,** include a crash report with a stack trace from the operating system. Make sure to add the crash report in the issue and place it in a [code block](https://help.github.com/en/articles/getting-started-with-writing-and-formatting-on-github#multiple-lines),
* **If you are reporting any ClearML crash,** include a crash report with a stack trace from the operating system. Make sure to add the crash report in the issue and place it in a [code block](https://help.github.com/en/articles/getting-started-with-writing-and-formatting-on-github#multiple-lines),
a [file attachment](https://help.github.com/articles/file-attachments-on-issues-and-pull-requests/), or just put it in a [gist](https://gist.github.com/) (and provide link to that gist).
* **Describe the behavior you observed after following the steps** and the exact problem with that behavior.
* **Explain which behavior you expected to see and why.**
* **For Web-App issues, please include screenshots and animated GIFs** which recreate the described steps and clearly demonstrate the problem. You can use [LICEcap](https://www.cockos.com/licecap/) to record GIFs on macOS and Windows, and [silentcast](https://github.com/colinkeenan/silentcast) or [byzanz](https://github.com/threedaymonk/byzanz) on Linux.
## Suggesting Enhancements
## Suggesting New Features and Enhancements
This section guides you through submitting an enhancement suggestion for TRAINS, including
completely new features and minor improvements to existing functionality.
By following these guidelines, you help maintainers and the community understand your suggestion and find related suggestions.
Enhancement suggestions are tracked as GitHub issues. After you determine which repository your enhancement suggestion is related to, create an issue on that repository and provide the following:
@@ -43,12 +44,18 @@ Enhancement suggestions are tracked as GitHub issues. After you determine which
* **A step-by-step description of the suggested enhancement** in as much detail as possible.
* **Specific examples to demonstrate the steps.** Include copy/pasteable snippets which you use in those examples as [Markdown code blocks](https://help.github.com/articles/markdown-basics/#multiple-lines).
* **Describe the current behavior and explain which behavior you expected to see instead and why.**
* **Include screenshots or animated GIFs** which help you demonstrate the steps or point out the part of TRAINS which the suggestion is related to. You can use [LICEcap](https://www.cockos.com/licecap/) to record GIFs on macOS and Windows, and [silentcast](https://github.com/colinkeenan/silentcast) or [byzanz](https://github.com/threedaymonk/byzanz) on Linux.
* **Include screenshots or animated GIFs** which help you demonstrate the steps or point out the part of ClearML which the suggestion is related to. You can use [LICEcap](https://www.cockos.com/licecap/) to record GIFs on macOS and Windows, and [silentcast](https://github.com/colinkeenan/silentcast) or [byzanz](https://github.com/threedaymonk/byzanz) on Linux.
## Pull Requests
Before you submit a new PR:
* Verify the work you plan to merge addresses an existing [issue](https://github.com/allegroai/clearml/issues) (If not, open a new one)
* Check related discussions in the [ClearML slack community](https://join.slack.com/t/allegroai-trains/shared_invite/enQtOTQyMTI1MzQxMzE4LTY5NTUxOTY1NmQ1MzQ5MjRhMGRhZmM4ODE5NTNjMTg2NTBlZGQzZGVkMWU3ZDg1MGE1MjQxNDEzMWU2NmVjZmY) (Or start your own discussion on the `#clearml-dev` channel)
* Make sure your code conforms to the ClearML coding standards by running:
`flake8 --max-line-length=120 --statistics --show-source --extend-ignore=E501 ./trains*`
In your PR include:
* A reference to the issue it addresses
* A brief description of the approach you've taken for implementing

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# ClearML introducing Dataset management!
## Decoupling Data from Code - The Dataset Paradigm
### The ultimate goal of `clearml-data` is to transform datasets into configuration parameters
Just like any other argument, the dataset argument should retrieve a full local copy of the
dataset to be used by the experiment.
This means datasets can be efficiently retrieved by any machine in a reproducible way.
Together it creates a full version control solution for all your data,
that is both machine and environment agnostic.
### Design Goals : Simple / Agnostic / File-based / Efficient
## Key Concepts:
1) **Dataset** is a **collection of files** : e.g. folder with all subdirectories and files included in the dataset
2) **Differential storage** : Efficient storage / network
3) **Flexible**: support addition / removal / merge of files and datasets
4) **Descriptive, transparent & searchable**: support projects, names, descriptions, tags and searchable fields
5) **Simple interface** (CLI and programmatic)
6) **Accessible**: get a copy of the dataset files from anywhere on any machine
### Workflow:
#### Simple dataset creation with CLI:
- Create a dataset
``` bash
clearml-data create --project <my_project> --name <my_dataset_name>
```
- Add local files to the dataset
``` bashtrue
clearml-data add --id <dataset_id_from_previous_command> --files ~/datasets/best_dataset/
```
- Upload files (Optional: specify storage `--storage` `s3://bucket` or `gs://` or `azure://` or `/mnt/shared/`)
``` bash
clearml-data upload --id <dataset_id>
```
- Close dataset
``` bash
clearml-data close --id <dataset_id>
```
#### Integrating datasets into your code:
``` python
from argparse import ArgumentParser
from clearml import Dataset
# adding command line interface, so it is easy to use
parser = ArgumentParser()
parser.add_argument('--dataset', default='aayyzz', type=str, help='Dataset ID to train on')
args = parser.parse_args()
# creating a task, so that later we could override the argparse from UI
task = Task.init(project_name='examples', task_name='dataset demo')
# getting a local copy of the dataset
dataset_folder = Datset.get(dataset_id=args.dataset).get_local_copy()
# go over the files in `dataset_folder` and train your model
```
#### Modifying a dataset with CLI:
- Create a new dataset (specify the parent dataset id)
``` bash
clearml-data create --name <improved_dataset> --parents <existing_dataset_id>
```
- Get a mutable copy of the current dataset
``` bash
clearml-data get --id <created_dataset_id> --copy ~/datasets/working_dataset
```
- Change / add / remove files from the dataset folder
``` bash
vim ~/datasets/working_dataset/everything.csv
```
- Sync local changes
``` bash
clearml-data sync --id <created_dataset_id> --folder ~/datasets/working_dataset
```
- Upload files (Optional: specify storage `--storage` `s3://bucket` or `gs://` or `azure://` or `/mnt/shared/`)
``` bash
clearml-data upload --id <created_dataset_id>
```
- Close dataset
``` bash
clearml-data close --id <created_dataset_id>
```
#### Command Line Interface Summary:
- **`search`** Search a dataset based on project / name / description / tag etc.
- **`list`** List the file directory content of a dataset (no need to download a copy pf the dataset)
- **`verify`** Verify a local copy of a dataset (verify the dataset files SHA2 hash)
- **`create`** Create a new dataset (support extending/inheriting multiple parents)
- **`delete`** Delete a dataset
- **`add`** Add local files to a dataset
- **`sync`** Sync dataset with a local folder (source-of-truth being the local folder)
- **`remove`** Remove files from dataset (no need to download a copy of the dataset)
- **`get`** Get a local copy of the dataset (either readonly --link, or writable --copy)
- **`upload`** Upload the dataset (use --storage to specify storage target such as S3/GS/Azure/Folder, default: file server)
#### Under the hood (how it all works):
Each dataset instance stores the collection of files added/modified from the previous version (parent).
When requesting a copy of the dataset all parent datasets on the graph are downloaded and a new folder
is merged with all changes introduced in the dataset DAG.
Implementation details:
Dataset differential snapshot is stored in a single zip file for efficiency in storage and network
bandwidth. Local cache is built into the process making sure datasets are downloaded only once.
Dataset contains SHA2 hash of all the files in the dataset.
In order to increase dataset fetching speed, only file size is verified automatically,
the SHA2 hash is verified only on user's request.
The design supports multiple parents per dataset, essentially merging all parents based on order.
To improve deep dataset DAG storage and speed, dataset squashing was introduced. A user can squash
a dataset, merging down all changes introduced in the DAG, creating a new flat version without parent datasets.
### Datasets UI:
A dataset is represented as a special `Task` in the system. <br>
It is of type `data-processing` with a special tag `dataset`.
- Full log (calls / CLI) of the dataset creation process can be found in the "Execution" section.
- Listing of the dataset differential snapshot, summary of files added / modified / removed and details of files
in the differential snapshot (location / size / hash), is available in the Artifacts section you can find a
- The full dataset listing (all files included) is available in the Configuration section under `Dataset Content`.
This allows you to quickly compare two dataset contents and visually see the difference.
- The dataset genealogy DAG and change-set summary table is visualized in Results / Plots
<a href="https://app.community.clear.ml"><img src="https://github.com/allegroai/clearml/blob/master/docs/dataset_screenshots.gif?raw=true" width="80%"></a>

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# TRAINS Explicit Logging
# ClearML Explicit Logging
Using the **TRAINS** [Logger](https://github.com/allegroai/trains/blob/master/trains/logger.py) module and other **TRAINS** features, you can explicitly log any of the following:
Using the **ClearML** [Logger](https://github.com/allegroai/clearml/blob/master/clearml/logger.py) module and other **ClearML** features, you can explicitly log any of the following:
* Report graphs and images
* [Scalar metrics](#scalar-metrics)
@@ -19,10 +19,10 @@ Using the **TRAINS** [Logger](https://github.com/allegroai/trains/blob/master/tr
* Message logging
* [Reporting text without formatting](#reporting-text-without-formatting)
Additionally, the **TRAINS** Logger module provides methods that allow you to do the following:
Additionally, the **ClearML** Logger module provides methods that allow you to do the following:
* Get the [current logger]()
* Overrride the TRAINS configuration file with a [default upload destination]() for images and files
* Overrride the ClearML configuration file with a [default upload destination]() for images and files
## Graphs and Images
@@ -30,7 +30,7 @@ Additionally, the **TRAINS** Logger module provides methods that allow you to do
Use to report scalar metrics by iteration as a line plot.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/scalar_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/scalar_reporting.py)) with the following method.
**Method**:
@@ -101,7 +101,7 @@ def report_scalar(self, title, series, value, iteration)
Use to report any data by iteration as a histogram.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method.
**Method**:
@@ -199,7 +199,7 @@ def report_histogram(self, title, series, values, iteration, labels=None, xlabel
Use to report any data by iteration as a single or multiple line plot.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method.
**Method**:
@@ -325,7 +325,7 @@ def report_line_plot(self, title, series, iteration, xaxis, yaxis, mode='lines',
Use to report any vector data as a 2D scatter diagram.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method.
**Method**:
@@ -461,7 +461,7 @@ def report_scatter2d(self, title, series, scatter, iteration, xaxis=None, yaxis=
Use to report any array data as a 3D scatter diagram.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/3d_plots_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/3d_plots_reporting.py)) with the following method.
**Method**:
@@ -597,7 +597,7 @@ def report_scatter3d(self, title, series, scatter, iteration, labels=None, mode=
Use to report a heat-map matrix as a confusion matrix. You can also plot a heat-map as a [surface diagram](#surface-diagrams).
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method.
**Method**:
@@ -689,7 +689,7 @@ def report_confusion_matrix(self, title, series, matrix, iteration, xlabels=None
Use to plot a heat-map matrix as a surface diagram. You can also plot a heat-map as a [confusion matrix](#confusion-matrices).
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/3d_plots_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/3d_plots_reporting.py)) with the following method.
**Method**:
@@ -818,10 +818,10 @@ def report_surface(self, title, series, matrix, iteration, xlabels=None, ylabels
### Images
Use to report an image and upload its contents to the bucket specified in the **TRAINS** configuration file,
Use to report an image and upload its contents to the bucket specified in the **ClearML** configuration file,
or a [a default upload destination](#set-default-upload-destination), if you set a default.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/manual_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/manual_reporting.py)) with the following method.
**Method**:
@@ -929,7 +929,7 @@ def report_image(self, title, series, iteration, local_path=None, matrix=None, m
### Logging Experiment Parameter Dictionaries
In order for **TRAINS** to log a dictionary of parameters, use the `Task.connect` method.
In order for **ClearML** to log a dictionary of parameters, use the `Task.connect` method.
For example, to log the hyper-parameters <code>learning_rate</code>, <code>batch_size</code>, <code>display_step</code>, <code>model_path</code>, <code>n_hidden_1</code>, and <code>n_hidden_2</code>:
@@ -938,27 +938,27 @@ For example, to log the hyper-parameters <code>learning_rate</code>, <code>batch
parameters_dict = { 'learning_rate': 0.001, 'batch_size': 100, 'display_step': 1,
'model_path': "/tmp/model.ckpt", 'n_hidden_1': 256, 'n_hidden_2': 256 }
# Connect the dictionary to your TRAINS Task
# Connect the dictionary to your ClearML Task
parameters_dict = Task.current_task().connect(parameters_dict)
```
### Specifying Environment Variables to Track
By setting the `TRAINS_LOG_ENVIRONMENT` environment variable, make **TRAINS** log either:
By setting the `CLEARML_LOG_ENVIRONMENT` environment variable, make **ClearML** log either:
* All environment variables
export TRAINS_LOG_ENVIRONMENT="*"
export CLEARML_LOG_ENVIRONMENT="*"
* Specific environment variables
For example, log `PWD` and `PYTHONPATH`
export TRAINS_LOG_ENVIRONMENT="PWD,PYTHONPATH"
export CLEARML_LOG_ENVIRONMENT="PWD,PYTHONPATH"
* No environment variables
export TRAINS_LOG_ENVIRONMENT=
export CLEARML_LOG_ENVIRONMENT=
## Logging Messages
@@ -972,7 +972,7 @@ Use the methods in this section to log various types of messages. The method nam
def debug(self, msg, *args, **kwargs)
```
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/text_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method.
**Arguments**:
@@ -1010,7 +1010,7 @@ First [get the current logger](#get-the-current-logger) and then use it (see an
def info(self, msg, *args, **kwargs)
```
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/text_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method.
**Arguments**:
@@ -1048,7 +1048,7 @@ First [get the current logger](#get-the-current-logger) and then use it (see an
def warn(self, msg, *args, **kwargs)
```
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/text_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method.
**Arguments**:<a name="log_arguments"></a>
@@ -1087,7 +1087,7 @@ First [get the current logger](#get-the-current-logger) and then use it (see an
def error(self, msg, *args, **kwargs)
```
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/text_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method.
**Arguments**:
@@ -1125,7 +1125,7 @@ First [get the current logger](#get-the-current-logger) and then use it (see an
def critical(self, msg, *args, **kwargs)
```
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/text_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method.
**Arguments**:
@@ -1163,7 +1163,7 @@ First [get the current logger](#get-the-current-logger) and then use it (see an
def fatal(self, msg, *args, **kwargs)
```
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/text_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method.
**Arguments**:
@@ -1201,7 +1201,7 @@ First [get the current logger](#get-the-current-logger) and then use it (see an
def console(self, msg, level=logging.INFO, omit_console=False, *args, **kwargs)
```
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/text_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method.
**Arguments**:
@@ -1279,7 +1279,7 @@ First [get the current logger](#get-the-current-logger) and then use it (see an
def report_text(self, msg, level=logging.INFO, print_console=False, *args, **_)
```
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/trains/blob/master/examples/reporting/text_reporting.py)) with the following method.
First [get the current logger](#get-the-current-logger) and then use it (see an [example script](https://github.com/allegroai/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method.
**Arguments**:
@@ -1371,7 +1371,7 @@ None.
Use to specify the default destination storage location used for uploading images.
Images are uploaded and a link to the image is reported.
Credentials for the storage location are in the global configuration file (for example, on Linux, <code>~/trains.conf</code>).
Credentials for the storage location are in the global configuration file (for example, on Linux, <code>~/clearml.conf</code>).
**Method**:

18
docs/trains.conf
View File

@@ -1,4 +1,4 @@
# TRAINS SDK configuration file
# ClearML SDK configuration file - Please use ~/clearml.conf
api {
# web_server on port 8080
web_server: "http://localhost:8080"
@@ -16,12 +16,12 @@ api {
verify_certificate: True
}
sdk {
# TRAINS - default SDK configuration
# ClearML - default SDK configuration
storage {
cache {
# Defaults to system temp folder / cache
default_base_dir: "~/.trains/cache"
default_base_dir: "~/.clearml/cache"
}
}
@@ -103,7 +103,7 @@ sdk {
google.storage {
# # Default project and credentials file
# # Will be used when no bucket configuration is found
# project: "trains"
# project: "clearml"
# credentials_json: "/path/to/credentials.json"
# # Specific credentials per bucket and sub directory
@@ -111,7 +111,7 @@ sdk {
# {
# bucket: "my-bucket"
# subdir: "path/in/bucket" # Not required
# project: "trains"
# project: "clearml"
# credentials_json: "/path/to/credentials.json"
# },
# ]
@@ -119,7 +119,7 @@ sdk {
azure.storage {
# containers: [
# {
# account_name: "trains"
# account_name: "clearml"
# account_key: "secret"
# # container_name:
# }
@@ -161,8 +161,8 @@ sdk {
# do not analyze the entire repository.
force_analyze_entire_repo: false
# If set to true, *trains* update message will not be printed to the console
# this value can be overwritten with os environment variable TRAINS_SUPPRESS_UPDATE_MESSAGE=1
# 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`
@@ -173,7 +173,7 @@ sdk {
# 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 TRAINS_LOG_ENVIRONMENT="[AWS_*, CUDA_VERSION]"
# 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: []