diff --git a/docs/clearml-task.md b/docs/clearml-task.md index e20c2e3a..782810cd 100644 --- a/docs/clearml-task.md +++ b/docs/clearml-task.md @@ -1,167 +1,6 @@ # `clearml-task` - Execute ANY python code on a remote machine Using only your command line and __zero__ additional lines of code, you can easily integrate the ClearML platform -into your experiment. With the `clearml-task` command, you can create a [Task](https://clear.ml/docs/latest/docs/fundamentals/task) -using any script from **any python code or repository and launch it on a remote machine**. +into your experiment with the `clearml-task` CLI tool. -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? - -With the `clearml-task` command, you specify the details of your experiment including: -* Project and task name -* Repository / commit / branch -* [Queue](https://clear.ml/docs/latest/docs/fundamentals/agents_and_queues#what-is-a-queue) - name -* 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. - -Then `clearml-task` does the rest of the heavy-lifting. It creates a new experiment or Task on your `clearml-server` -according to your specifications, and then, it will enqueue the experiment to the selected execution queue. - -While the Task 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. During and after the Task execution, you can -track and visualize the results in the ClearML Web UI. - -### 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 to `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` installed. `clearml` also has a [Task](https://clear.ml/docs/latest/docs/fundamentals/task) - feature but it requires two lines of code in order to integrate the platform. -- `clearml-agent` running on at least one machine (to execute the experiment) - -## Tutorial - -### Launching a job from a repository - -You will be launching this [script](https://github.com/allegroai/events/blob/master/webinar-0620/keras_mnist.py) -on a remote machine. You will be using the following command-line options: -1. Give the experiment a name and select a project, for example: `--project keras_examples --name remote_test`. If the project - doesn't exist, a new project will be created with the selected name. -2. Select the repository with your code. For example: `--repo https://github.com/allegroai/events.git` You can specify a - branch and/or commit using `--branch --commit `. If you do not specify the - branch / commit, it will use by default the latest commit from the master branch, -3. Specify which script in the repository needs to be run, for example: `--script /webinar-0620/keras_mnist.py` -By default, the execution working directory will be the root of the repository. If you need to change it, - add `--cwd ` -4. If you need, pass an argument to your scripts, use `--args`, followed by the arguments. - The names of the arguments should match the argparse arguments, but without the '--' prefix. Instead - of --key=value -> use `--args key=value`, for example `--args batch_size=64 epochs=1` -5. Select the queue for your Task's execution, for example: `--queue default`. If a queue isn't chosen, the Task - will not be executed, it will be left in [draft mode](https://clear.ml/docs/latest/docs/fundamentals/task#task-states), - and you can enqueue and execute the Task at a later point. -6. Add required packages. If your repo has a requirements.txt file, you don't need to do anything; `clearml-task` - will automatically find the file and put it in your Task. If your repo does __not__ have a requirements file and -there are packages that are necessary for the execution of your code, use --packages . For example: - `--packages "keras" "tensorflow>2.2"`. - -``` bash -clearml-task --project keras_examples --name remote_test --repo https://github.com/allegroai/events.git ---script /webinar-0620/keras_mnist.py --args batch_size=64 epochs=1 --queue default -``` - - -### Launching a job from a local script - -You will be launching a single local script file (no git repo needed) on a remote machine: - -1. Give the experiment a name and select a project (`--project examples --name remote_test`) -2. Select the script file on your machine, `--script /path/to/my/script.py` -3. If you require specific packages to run your code, you can specify them manually with `--packages "package_name" "package_name2`, - for example: `packages "keras" "tensorflow>2.2"` - or you can pass a requirements file `--requirements /path/to/my/requirements.txt` -4. If you need to pass arguments, like in the repo case, add `--args key=value` and make sure that the key names match - the argparse arguments (`--args batch_size=64 epochs=1`) -5. If you have a docker container with an entire environment in which you want your script to run inside, - add e.g. `--docker nvcr.io/nvidia/pytorch:20.11-py3` -6. Select the queue for your Task's execution, for example: `--queue dual_gpu`. If a queue isn't chosen, the Task - will not be executed, it will be left in [draft mode](https://clear.ml/docs/latest/docs/fundamentals/task#task-states), - and you can enqueue and execute it at a later point. - -``` bash -clearml-task --project examples --name remote_test --script /path/to/my/script.py ---packages "keras" "tensorflow>2.2" --args epochs=1 batch_size=64 ---queue dual_gpu -``` - -### CLI options - -``` bash -clearml-task --help -``` - -``` console -ClearML launch - launch any codebase on remote machine running clearml-agent - -optional arguments: - -h, --help show this help message and exit - --version Display the clearml-task 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 - = 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 - --docker_args DOCKER_ARGS - Add docker arguments, pass a single string - --docker_bash_setup_script DOCKER_BASH_SETUP_SCRIPT - Add bash script to be executed inside the docker - before setting up the Task's environment - --output-uri OUTPUT_URI - Optional: set the Task `output_uri` (automatically - upload model destination) - --task-type TASK_TYPE - Set the Task type, optional values: training, testing, - inference, data_processing, application, monitor, - controller, optimizer, service, qc, custom - --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. - -``` +For more information, see the [ClearML Documentation](https://clear.ml/docs/latest/docs/apps/clearml_task/). \ No newline at end of file diff --git a/docs/dataset_screenshots.gif b/docs/dataset_screenshots.gif deleted file mode 100644 index 759985ce..00000000 Binary files a/docs/dataset_screenshots.gif and /dev/null differ diff --git a/docs/datasets.md b/docs/datasets.md index a2e8b1dc..a24fd6d8 100644 --- a/docs/datasets.md +++ b/docs/datasets.md @@ -1,159 +1,5 @@ # 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 --name -``` -- Add local files to the dataset -``` bash -clearml-data add --files ~/datasets/best_dataset/ -``` -- Close dataset and upload files (Optional: specify storage `--storage` `s3://bucket`, `gs://`, `azure://` or `/mnt/shared/`) -``` bash -clearml-data close --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 = Dataset.get(dataset_id=args.dataset).get_local_copy() - -# go over the files in `dataset_folder` and train your model -``` - -#### Create dataset from code -Creating datasets from code is especially helpful when some preprocessing is done on raw data and we want to save -preprocessing code as well as dataset in a single Task. - -```python -from clearml import Dataset - -# Preprocessing code here - -dataset = Dataset.create(dataset_name='dataset name',dataset_project='dataset project') -dataset.add_files('/path_to_data') -dataset.upload() -dataset.finalize() - -``` - -#### Modifying a dataset with CLI: - -- Create a new dataset (specify the parent dataset id) -```bash -clearml-data create --name --parents -``` -- Get a mutable copy of the current dataset -```bash -clearml-data get --id --copy ~/datasets/working_dataset -``` -- Change / add / remove files from the dataset folder -```bash -vim ~/datasets/working_dataset/everything.csv -``` - -#### Folder sync mode - -Folder sync mode updates dataset according to folder content changes.
-This is useful in case there's a single point of truth, either a local or network folder that gets updated periodically. -When using `clearml-data sync` and specifying parent dataset, the folder changes will be reflected in a new dataset version. -This saves time manually updating (adding \ removing) files. - -- Sync local changes -``` bash -clearml-data sync --id --folder ~/datasets/working_dataset -``` -- Upload files (Optional: specify storage `--storage` `s3://bucket`, `gs://`, `azure://`, `/mnt/shared/`) -``` bash -clearml-data upload --id -``` -- Close dataset -``` bash -clearml-data close --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.
-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 +Simplify data management with ClearML: create, version, and access datasets from anywhere, ensuring traceability and reproducibility. +For more information, see the [ClearML Documentation](https://clear.ml/docs/latest/docs/clearml_data/). \ No newline at end of file diff --git a/docs/logger.md b/docs/logger.md index fbe0360a..257b4894 100644 --- a/docs/logger.md +++ b/docs/logger.md @@ -1,1410 +1,6 @@ # ClearML Explicit Logging -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) - * [Histograms](#histograms) - * [Line plots](#line-plots) - * [2D scatter diagrams](#2d-scatter-diagrams) - * [3D scatter diagrams](#3d-scatter-diagrams) - * [Confusion matrices](#confusion-matrices) - * [Surface diagrams](#surface-diagrams) - * [Images](#images) - -* Track hyperparameters and OS environment variables - * Logging experiment parameter [dictionaries](#logging-experiment-parameter-dictionaries) - * Specifying [environment variables](#specifying-environment-variables-to-track) to track - -* Message logging - * [Reporting text without formatting](#reporting-text-without-formatting) - -Additionally, the **ClearML** Logger module provides methods that allow you to do the following: - - * Get the [current logger]() - * Override the ClearML configuration file with a [default upload destination]() for images and files - -## Graphs and Images - -### Scalar Metrics - -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/clearml/blob/master/examples/reporting/scalar_reporting.py)) with the following method. - -**Method**: - -```python -def report_scalar(self, title, series, value, iteration) -``` - -**Arguments**: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
iteration - integer - The iteration number (x-axis). - Yes -
series - string - The data series label. - Yes -
title - string - The plot title. - Yes -
value - float - The scalar metric data value (y-axis). - Yes -
- -### Histograms - -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/clearml/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method. - -**Method**: - -```python -def report_histogram(self, title, series, values, iteration, labels=None, xlabels=None) -``` - -**Arguments**: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
iteration - integer - The iteration number (x-axis). - Yes -
series - string - The data series label. - Yes -
title - string - The plot title. - Yes -
values - Either: -
    -
  • list of float -
    • -
  • numpy array -
    • -
- 		The histogram data values (y-axis). - Yes -
labels - list of strings - Labels for each bar group in the histogram. The default value is None. - No -
xlabels - list of strings - Labels for each bucket in the histogram. Each label in the xlabels list corresponds to a value in the values list (or numpy array). The default value is None. - No -
- - -### Line Plots - -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/clearml/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method. - -**Method**: - -```python -def report_line_plot(self, title, series, iteration, xaxis, yaxis, mode='lines', reverse_xaxis=False, comment=None) -``` - -**Arguments**: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
iteration - integer - The iteration number (x-axis). - Yes -
series - LineSeriesInfo - One (single line plot) or more (multiple line plot) series of data. - Yes -
title - string - The plot title. - Yes -
comment - string - Any text (e.g., subtitle or other comment) which displays under the plot title. The default value is None. - No -
mode - string - Type of line plot. The values are: -
    -
  • lines - lines connecting data points (default) -
    • -
  • markers - markers for each data point -
    • -
  • lines+markers - lines and markers -
    • -
- 		No -
reverse_xaxis - boolean - Indicates whether to display the x-axis values in ascending or descending order. The values are: -
    -
  • True - descending order -
    • -
  • False - ascending order (default) -
    • -
- 		No -
xaxis - string - x-axis title. - No -
yaxis - string - y-axis title. - No -
- - -### 2D Scatter Diagrams - -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/clearml/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method. - -**Method**: - -```python -def report_scatter2d(self, title, series, scatter, iteration, xaxis=None, yaxis=None, labels=None, mode='lines', comment=None) -``` - -**Arguments**: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
iteration - integer - The iteration number (x-axis). - Yes -
scatter - Either: -
    -
  • list of (pairs of x, y) -
    • -
  • ndarray -
    • -
- 		The scatter data. For example, a list of pairs in the form: [(x1,y1),(x2,y2),(x3,y3),...]. - Yes -
series - string - The data series label. - Yes -
title - string - The plot title. - Yes -
comment - string - Any text (e.g., subtitle or other comment) which displays under the plot title. The default value is None. - No -
labels - list of strings - Label text per data point in the scatter diagram. The default value is None. - No -
mode - string - Type of 2D scatter diagram. The values are: -
    -
  • lines - lines connecting data points (default) -
    • -
  • markers - markers for each data point -
    • -
  • lines+markers - lines and markers -
    • -
- 		No -
xaxis - string - x-axis title. The default value is None. - No -
yaxis - string - y-axis title. The default value is None. - No -
- - -### 3D Scatter Diagrams - -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/clearml/blob/master/examples/reporting/3d_plots_reporting.py)) with the following method. - -**Method**: - -```python -def report_scatter3d(self, title, series, scatter, iteration, labels=None, mode='markers', fill=False, comment=None) -``` - -**Argument**: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
iteration - integer - The iteration number (x-axis). - Yes -
scatter - Either: -
    -
  • list of (pairs of x, y, z) -
    • -
  • ndarray -
    • -
  • list of series [[(x1,y1,z1)...]] -
    • -
- 		The scatter data. For example, a list of series in the form: - [[(x1,y1,z1),(x2,y2,z2),...],[(x3,y3,z3),(x4,y4,z4),...],[(x5,y5,z5),(x6,y6,z6),...]] - Yes -
series - string - The data series label. - Yes -
title - string - The plot title. - Yes -
comment - string - Any text (e.g., subtitle or other comment) which displays under the plot title. The default value is None. - No -
fill - boolean - Indicates whether to fill the area under the scatter diagram. The values are: -
    -
  • True - fill -
    • -
  • False - do not fill (default) -
    • -
- 		No -
labels - list of strings - Label text per data point in the scatter. The default value is None. - No -
mode - string - Type of 3D scatter diagram. The values are: -
    -
  • lines -
    • -
  • markers -
    • -
  • lines+markers -
    • -
- The default value is lines. - 		No -
- - -### Confusion Matrices - -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/clearml/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py)) with the following method. - -**Method**: - -```python -def report_confusion_matrix(self, title, series, matrix, iteration, xlabels=None, ylabels=None, comment=None) -``` - -**Arguments**: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
iteration - integer - The iteration number (x-axis). - Yes -
matrix - ndarray - A heat-map matrix. - Yes -
series - string - The data series label. - Yes -
title - string - The plot title. - Yes -
xlabels - list of strings - Label per column of the matrix. The default value is None. - No -
ylabels - list of strings - Label per row of the matrix. The default value is None. - No -
- - -### Surface Diagrams - -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/clearml/blob/master/examples/reporting/3d_plots_reporting.py)) with the following method. - -**Method**: - -```python -def report_surface(self, title, series, matrix, iteration, xlabels=None, ylabels=None, xaxis=None, yaxis=None, camera=None, comment=None) -``` - -**Arguments**: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
iteration - integer - The iteration number (x-axis). - Yes -
matrix - ndarray - A heat-map matrix. - Yes -
series - string - The data series label. - Yes -
title - string - The plot title. - Yes -
camera - tuple - The position of the camera as (x, y, x), if applicable. - No -
comment - string - Any text (e.g., subtitle or other comment) which displays under the plot title. The default value is None. - No -
xlabels - list of strings - Label per column of the matrix. The default value is None. - No -
xaxis - string - x-axis title. - No -
ylabels - list of strings - Label per row of the matrix. The default value is None. - No -
yaxis - string - y-axis title. - No -
- -### Images - -Report an image and upload its contents to the bucket specified in the **ClearML** configuration file, -or 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/clearml/blob/master/examples/manual_reporting.py)) with the following method. - -**Method**: - -```python -def report_image(self, title, series, iteration, local_path=None, matrix=None, max_image_history=None, delete_after_upload=False) -``` - -**Arguments**: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
iteration - integer - The iteration number. - Yes -
series - string - The label of the series. - Yes -
title - string - The title of the image. - Yes -
delete_after_upload - boolean - Indicates whether to delete the local copy of the file after uploading it. The values are: -
    -
  • True - delete -
    • -
  • False - do not delete (default) -
    • -
- 		No -
matrix - ndarray - A 3D numpy.ndarray object containing image data (RGB). If path is not specified, then matrix is required. The default value is None. - No -
max_image_history - integer - The maximum number of images to store per metric / variant combination. For an unlimited number of images to store, specify a negative number. - The default value which is set in the global configuration is 5. - No -
local_path - string - The path of the image file. If matrix is not specified, then path is required. The default value is None. - No -
- -## Hyperparameters and Environment Variables - -### Logging Experiment Parameter Dictionaries - -In order for **ClearML** to log a dictionary of parameters, use the `Task.connect` method. - -For example, to log the hyperparameters learning_rate, batch_size, display_step, model_path, n_hidden_1, and n_hidden_2: - -```python -# Create a dictionary of parameters -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 ClearML Task -parameters_dict = Task.current_task().connect(parameters_dict) -``` - -### Specifying Environment Variables to Track - -By setting the `CLEARML_LOG_ENVIRONMENT` environment variable, make **ClearML** log either: - -* All environment variables - - export CLEARML_LOG_ENVIRONMENT=* - -* Specific environment variables - - For example, log `PWD` and `PYTHONPATH` - - export CLEARML_LOG_ENVIRONMENT=PWD,PYTHONPATH - -* No environment variables - - export CLEARML_LOG_ENVIRONMENT= - -## Logging Messages - -Use the methods in this section to log various types of messages. The method name describes the type of message. - -### Debugging Messages - -**Method**: - -```python -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/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method. - -**Arguments**: - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
msg - string - The text to log. - Yes -
- -### Informational Messages - -**Method**: - -```python -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/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method. - -**Arguments**: - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
msg - string - The text to log. - Yes -
- -### Warnings - -**Method**: - -```python -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/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method. - -**Arguments**: - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
msg - string - The text to log. - Yes -
- -### General Errors - -**Method**: - -```python -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/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method. - -**Arguments**: - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
msg - string - The text to log. - Yes -
- -### Critical Errors - -**Method**: - -```python -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/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method. - -**Arguments**: - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
msg - string - The text to log. - Yes -
- -### Fatal Errors - -**Method**: - -```python -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/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method. - -**Arguments**: - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
msg - string - The text to log. - Yes -
- -### Console and Logger Messages - -**Method**: - -```python -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/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method. - -**Arguments**: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
msg - string - The text to log. - Yes -
level - string - The log level. The values are: -
    -
  • logging.DEBUG -
    • -
  • logging.INFO -
    • -
  • logging.WARNING -
    • -
  • logging.ERROR -
    • -
  • logging.FATAL -
    • -
  • logging.CRITICAL -
    • -
- 		No -
omit_console - boolean - Indicates whether to send the message to the log only. The values are: -
    -
  • True - send the message to the log only -
    • -
  • False - send the message to the console and the log -
    • -
- 		No -
- -### Reporting Text Without Formatting - -**Method**: - -```python -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/clearml/blob/master/examples/reporting/text_reporting.py)) with the following method. - -**Arguments**: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
msg - string - The text to log. - Yes -
level - string - The log level. The values are: -
    -
  • logging.DEBUG -
    • -
  • logging.INFO -
    • -
  • logging.WARNING -
    • -
  • logging.ERROR -
    • -
  • logging.FATAL -
    • -
  • logging.CRITICAL -
    • -
- 		No -
print_console - boolean - Indicates whether to log to the console, in addition to the log. The values are: -
    -
  • True - print to the console and log -
    • -
  • False - print to the log, only (default) -
    • -
- 		No -
- -## Logger Object and Storage Methods - -### Get the Current Logger - -Use to return a reference to the current logger object. - -**Method**: - -```python -def current_logger(cls) -``` - -**Arguments**: - -None. - -### Set Default Upload Destination - -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, ~/clearml.conf). - -**Method**: - -```python -def set_default_upload_destination(self, uri) -``` - -**Arguments**: - - - - - - - - - - - - - - - - - - -
Parameter - Type - Description - Mandatory -
uri - string - The destination storage location, for example s3://bucket/directory/ or file:///tmp/debug/. - Yes -
+Using the [Logger](https://github.com/allegroai/clearml/blob/master/clearml/logger.py) module and other ClearML features, +you can explicitly log scalars, text, plots, videos, and more. +For more information see the [ClearML Documentation](https://clear.ml/docs/latest/docs/fundamentals/logger). \ No newline at end of file diff --git a/docs/results_screenshots.gif b/docs/results_screenshots.gif deleted file mode 100644 index 1b4ca59c..00000000 Binary files a/docs/results_screenshots.gif and /dev/null differ diff --git a/docs/screenshots/compare_plots.png b/docs/screenshots/compare_plots.png deleted file mode 100644 index cef2b457..00000000 Binary files a/docs/screenshots/compare_plots.png and /dev/null differ diff --git a/docs/screenshots/compare_plots_hist.png b/docs/screenshots/compare_plots_hist.png deleted file mode 100644 index 7715e1a2..00000000 Binary files a/docs/screenshots/compare_plots_hist.png and /dev/null differ diff --git a/docs/screenshots/compare_values.png b/docs/screenshots/compare_values.png deleted file mode 100644 index 281eaa27..00000000 Binary files a/docs/screenshots/compare_values.png and /dev/null differ diff --git a/docs/screenshots/custom_column.png b/docs/screenshots/custom_column.png deleted file mode 100644 index 823dc320..00000000 Binary files a/docs/screenshots/custom_column.png and /dev/null differ diff --git a/docs/screenshots/set_custom_column.png b/docs/screenshots/set_custom_column.png deleted file mode 100644 index e41990a7..00000000 Binary files a/docs/screenshots/set_custom_column.png and /dev/null differ