ClearML/clearml
1
0
Fork 0
mirror of https://github.com/clearml/clearml synced 2025-01-31 17:17:00 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity
baa1f0a459
clearml/docs/faq.md
allegroai baa1f0a459 Documentation
2019-07-15 21:52:15 +03:00

319 lines
13 KiB
Markdown
Raw Blame History

# TRAINS FAQ
General Information
* [How do I know a new version came out?](#new-version-auto-update)
Configuration
* [How can I change the location of TRAINS configuration file?](#change-config-path)
* [How can I override TRAINS credentials from the OS environment?](#credentials-os-env)
Models
* [How can I sort models by a certain metric?](#custom-columns)
* [Can I store more information on the models?](#store-more-model-info)
* [Can I store the model configuration file as well?](#store-model-configuration)
* [I am training multiple models at the same time, but I only see one of them. What happened?](#only-last-model-appears)
* [Can I log input and output models manually?](#manually-log-models)
Experiments
* [I noticed I keep getting the message `warning: uncommitted code`. What does it mean?](#uncommitted-code-warning)
* [I do not use Argparser for hyper-parameters. Do you have a solution?](#dont-want-argparser)
* [I noticed that all of my experiments appear as `Training`. Are there other options?](#other-experiment-types)
* [Sometimes I see experiments as running when in fact they are not. What's going on?](#experiment-running-but-stopped)
* [My code throws an exception, but my experiment status is not "Failed". What happened?](#exception-not-failed)
* [When I run my experiment, I get an SSL Connection error [CERTIFICATE_VERIFY_FAILED]. Do you have a solution?](#ssl-connection-error)
Graphs and Logs
* [The first log lines are missing from the experiment log tab. Where did they go?](#first-log-lines-missing)
* [Can I create a graph comparing hyper-parameters vs model accuracy?](#compare-graph-parameters)
* [I want to add more graphs, not just with Tensorboard. Is this supported?](#more-graph-types)
GIT and Storage
* [Is there something TRAINS can do about uncommitted code running?](#help-uncommitted-code)
* [I read there is a feature for centralized model storage. How do I use it?](#centralized-model-storage)
* [When using PyCharm to remotely debug a machine, the git repo is not detected. Do you have a solution?](#pycharm-remote-debug-detect-git)
* [Git is not well supported in Jupyter, so we just gave up on committing our code. Do you have a solution?](#commit-git-in-jupyter)
Jupyter and scikit-learn
* [I am using Jupyter Notebook. Is this supported?](#jupyter-notebook)
* [Can I use TRAINS with scikit-learn?](#use-scikit-learn)
* Also see, [Git and Jupyter](#commit-git-in-jupyter)
## General Information
### How do I know a new version came out? <a name="new-version-auto-update"></a>
Starting v0.9.3 TRAINS notifies on a new version release.
Example, new client version available
```bash
TRAINS new package available: UPGRADE to vX.Y.Z is recommended!
```
Example, new server version available
```bash
TRAINS-SERVER new version available: upgrade to vX.Y is recommended!
```
## Configuration
### How can I change the location of TRAINS configuration file? <a name="change-config-path"></a>
Set "TRAINS_CONFIG_FILE" OS environment variable to override the default configuration file location.
```bash
export TRAINS_CONFIG_FILE="/home/user/mytrains.conf"
```
### How can I override TRAINS credentials from the OS environment? <a name="credentials-os-env"></a>
Set the OS environment variables below, in order to override the configuration file / defaults.
```bash
export TRAINS_API_ACCESS_KEY="key_here"
export TRAINS_API_SECRET_KEY="secret_here"
export TRAINS_API_HOST="http://localhost:8008"
```
## Models
### How can I sort models by a certain metric? <a name="custom-columns"></a>
Models are associated with the experiments that created them.
In order to sort experiments by a specific metric, add a custom column in the experiments table,
<img src="https://github.com/allegroai/trains/blob/master/docs/screenshots/set_custom_column.png?raw=true" width=25%>
<img src="https://github.com/allegroai/trains/blob/master/docs/screenshots/custom_column.png?raw=true" width=25%>
### Can I store more information on the models? <a name="store-more-model-info"></a>
#### For example, can I store enumeration of classes?
Yes! Use the `Task.set_model_label_enumeration()` method:
```python
Task.current_task().set_model_label_enumeration( {"label": int(0), } )
```
### Can I store the model configuration file as well? <a name="store-model-configuration"></a>
Yes! Use the `Task.set_model_design()` method:
```python
Task.current_task().set_model_design("a very long text with the configuration file's content")
```
### I am training multiple models at the same time, but I only see one of them. What happened? <a name="only-last-model-appears"></a>
All models can be found under the project's **Models** tab,
that said, currently in the Experiment's information panel TRAINS shows only the last associated model.
This will be fixed in a future version.
### Can I log input and output models manually? <a name="manually-log-models"></a>
Yes! For example:
```python
input_model = InputModel.import_model(link_to_initial_model_file)
Task.current_task().connect(input_model)
OutputModel(Task.current_task()).update_weights(link_to_new_model_file_here)
```
See [InputModel](https://github.com/allegroai/trains/blob/master/trains/model.py#L319) and [OutputModel](https://github.com/allegroai/trains/blob/master/trains/model.py#L539) for more information.
## Experiments
### I noticed I keep getting the message `warning: uncommitted code`. What does it mean? <a name="uncommitted-code-warning"></a>
TRAINS not only detects your current repository and git commit,
but also warns you if you are using uncommitted code. TRAINS does this
because uncommitted code means this experiment will be difficult to reproduce.
If you still don't care, just ignore this message - it is merely a warning.
### I do not use Argparser for hyper-parameters. Do you have a solution? <a name="dont-want-argparser"></a>
Yes! TRAINS supports using a Python dictionary for hyper-parameter logging. Just use:
```python
parameters_dict = Task.current_task().connect(parameters_dict)
```
From this point onward, not only are the dictionary key/value pairs stored as part of the experiment, but any changes to the dictionary will be automatically updated in the task's information.
### I noticed that all of my experiments appear as `Training`. Are there other options? <a name="other-experiment-types"></a>
Yes! When creating experiments and calling `Task.init`, you can provide an experiment type.
The currently supported types are `Task.TaskTypes.training` and `Task.TaskTypes.testing`. For example:
```python
task = Task.init(project_name, task_name, Task.TaskTypes.testing)
```
If you feel we should add a few more, let us know in the [issues](https://github.com/allegroai/trains/issues) section.
### Sometimes I see experiments as running when in fact they are not. What's going on? <a name="experiment-running-but-stopped"></a>
TRAINS monitors your Python process. When the process exits in an orderly fashion, TRAINS closes the experiment.
When the process crashes and terminates abnormally, the stop signal is sometimes missed. In such a case, you can safely right click the experiment in the Web-App and stop it.
## My code throws an exception, but my experiment status is not "Failed". What happened? <a name="exception-not-failed"></a>
This issue was resolved in v0.9.2. Upgrade TRAINS:
```pip install -U trains```
## When I run my experiment, I get an SSL Connection error [CERTIFICATE_VERIFY_FAILED]. Do you have a solution? <a name="ssl-connection-error"></a>
Your firewall may be preventing the connection. Try one of the following solutons:
* Direct python "requests" to use the enterprise certificate file by setting the OS environment variables CURL_CA_BUNDLE or REQUESTS_CA_BUNDLE.
You can see a detailed discussion at [https://stackoverflow.com/questions/48391750/disable-python-requests-ssl-validation-for-an-imported-module](https://stackoverflow.com/questions/48391750/disable-python-requests-ssl-validation-for-an-imported-module).
2. Disable certificate verification (for security reasons, this is not recommended):
1. Upgrade TRAINS to the current version:
```pip install -U trains```
1. Create a new **trains.conf** configuration file (sample file [here](https://github.com/allegroai/trains/blob/master/docs/trains.conf)), containing:
```api { verify_certificate = False }```
1. Copy the new **trains.conf** file to ~/trains.conf (on Windows: C:\Users\your_username\trains.conf)
## Graphs and Logs
### The first log lines are missing from the experiment log tab. Where did they go? <a name="first-log-lines-missing"></a>
Due to speed/optimization issues, we opted to display only the last several hundred log lines.
You can always downloaded the full log as a file using the Web-App.
### Can I create a graph comparing hyper-parameters vs model accuracy? <a name="compare-graph-parameters"></a>
Yes, you can manually create a plot with a single point X-axis for the hyper-parameter value,
and Y-Axis for the accuracy. For example:
```python
number_layers = 10
accuracy = 0.95
Task.current_task().get_logger().report_scatter2d(
"performance", "accuracy", iteration=0,
mode='markers', scatter=[(number_layers, accuracy)])
```
Assuming the hyper-parameter is "number_layers" with current value 10, and the accuracy for the trained model is 0.95.
Then, the experiment comparison graph shows:
<img src="https://github.com/allegroai/trains/blob/master/docs/screenshots/compare_plots.png?raw=true" width="50%">
Another option is a histogram chart:
```python
number_layers = 10
accuracy = 0.95
Task.current_task().get_logger().report_vector(
"performance", "accuracy", iteration=0, labels=['accuracy'],
values=[accuracy], xlabels=['number_layers %d' % number_layers])
```
<img src="https://github.com/allegroai/trains/blob/master/docs/screenshots/compare_plots_hist.png?raw=true" width="50%">
### I want to add more graphs, not just with Tensorboard. Is this supported? <a name="more-graph-types"></a>
Yes! Use a [Logger](https://github.com/allegroai/trains/blob/master/trains/logger.py) object. An instance can be always be retrieved using the `Task.current_task().get_logger()` method:
```python
# Get a logger object
logger = Task.current_task().get_logger()
# Report some scalar
logger.report_scalar("loss", "classification", iteration=42, value=1.337)
```
#### **TRAINS supports:**
* Scalars
* Plots
* 2D/3D Scatter Diagrams
* Histograms
* Surface Diagrams
* Confusion Matrices
* Images
* Text logs
For a more detailed example, see [here](https://github.com/allegroai/trains/blob/master/examples/manual_reporting.py).
## Git and Storage
### Is there something TRAINS can do about uncommitted code running? <a name="help-uncommitted-code"></a>
Yes! TRAINS currently stores the git diff as part of the experiment's information.
The Web-App will soon present the git diff as well. This is coming very soon!
### I read there is a feature for centralized model storage. How do I use it? <a name="centralized-model-storage"></a>
When calling `Task.init()`, providing the `output_uri` parameter allows you to specify the location in which model snapshots will be stored.
For example, calling:
```python
task = Task.init(project_name, task_name, output_uri="/mnt/shared/folder")
```
Will tell TRAINS to copy all stored snapshots into a sub-folder under `/mnt/shared/folder`.
The sub-folder's name will contain the experiment's ID.
Assuming the experiment's ID in this example is `6ea4f0b56d994320a713aeaf13a86d9d`, the following folder will be used:
`/mnt/shared/folder/task_6ea4f0b56d994320a713aeaf13a86d9d/models/`
TRAINS supports more storage types for `output_uri`:
```python
# AWS S3 bucket
task = Task.init(project_name, task_name, output_uri="s3://bucket-name/folder")
```
```python
# Google Cloud Storage bucket
taks = Task.init(project_name, task_name, output_uri="gs://bucket-name/folder")
```
**NOTE:** These require configuring the storage credentials in `~/trains.conf`.
For a more detailed example, see [here](https://github.com/allegroai/trains/blob/master/docs/trains.conf#L55).
### When using PyCharm to remotely debug a machine, the git repo is not detected. Do you have a solution? <a name="pycharm-remote-debug-detect-git"></a>
Yes! Since this is such a common occurrence, we created a PyCharm plugin that allows a remote debugger to grab your local repository / commit ID. See our [TRAINS PyCharm Plugin](https://github.com/allegroai/trains-pycharm-plugin) repository for instructions and [latest release](https://github.com/allegroai/trains-pycharm-plugin/releases).
### Git is not well supported in Jupyter, so we just gave up on committing our code. Do you have a solution? <a name="commit-git-in-jupyter"></a>
Yes! Check our [TRAINS Jupyter Plugin](https://github.com/allegroai/trains-jupyter-plugin). This plugin allows you to commit your notebook directly from Jupyter. It also saves the Python version of your code and creates an updated `requirements.txt` so you know which packages you were using.
## Jupyter and scikit-learn
### I am using Jupyter Notebook. Is this supported? <a name="jupyter-notebook"></a>
Yes! Jupyter Notebook is supported. See [TRAINS Jupyter Plugin](https://github.com/allegroai/trains-jupyter-plugin).
### Can I use TRAINS with scikit-learn? <a name="use-scikit-learn"></a>
Yes! `scikit-learn` is supported. Everything you do is logged.
**NOTE**: Models are not automatically logged because in most cases, scikit-learn will simply pickle the object to files so there is no underlying frame we can connect to.
Reference in New Issue View Git Blame Copy Permalink