From 05c75e088d9ef0de50c6ad5939717a7a51e7c650 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pawe=C5=82=20Redzy=C5=84ski?= Date: Tue, 2 Mar 2021 19:22:02 +0100 Subject: [PATCH] dvclive docs: add usage with DVC section --- content/docs/dvclive/usage-with-dvc.md | 125 +++++++++++++++++++++++++ content/docs/dvclive/usage.md | 40 +++++--- static/img/dvclive_report.png | Bin 0 -> 131331 bytes 3 files changed, 152 insertions(+), 13 deletions(-) create mode 100644 content/docs/dvclive/usage-with-dvc.md create mode 100644 static/img/dvclive_report.png diff --git a/content/docs/dvclive/usage-with-dvc.md b/content/docs/dvclive/usage-with-dvc.md new file mode 100644 index 0000000000..51cf336514 --- /dev/null +++ b/content/docs/dvclive/usage-with-dvc.md @@ -0,0 +1,125 @@ +# dvclive with DVC + +Even though dvclive does not require DVC to function properly, it includes a lot +of integrations with DVC that user might find valuable. In this section we will +modify the [example from previous one](/doc/dvclive/usage) to see how DVC can +cooperate with dvclive. + +Let's use the code prepared in previous example and try to make it work with +dvc. Training file `train.py` content: + +```python +from keras.datasets import mnist +from keras.models import Sequential +from keras.layers.core import Dense, Activation +from keras.utils import np_utils + +def load_data(): + (x_train, y_train), (x_test, y_test) = mnist.load_data() + + x_train = x_train.reshape(60000, 784) + x_test = x_test.reshape(10000, 784) + x_train = x_train.astype('float32') + x_test = x_test.astype('float32') + x_train /= 255 + x_test /= 255 + classes = 10 + y_train = np_utils.to_categorical(y_train, classes) + y_test = np_utils.to_categorical(y_test, classes) + return (x_train, y_train), (x_test, y_test) + +def get_model(): + model = Sequential() + model.add(Dense(512, input_dim=784)) + model.add(Activation('relu')) + + model.add(Dense(10, input_dim=512)) + + model.add(Activation('softmax')) + + model.compile(loss='categorical_crossentropy', + metrics=['accuracy'], optimizer='sgd') + return model + + +from keras.callbacks import Callback +import dvclive + +class MetricsCallback(Callback): + def on_epoch_end(self, epoch: int, logs: dict = None): + logs = logs or {} + for metric, value in logs.items(): + dvclive.log(metric, value) + dvclive.next_step() + +(x_train, y_train), (x_test, y_test) = load_data() +model = get_model() + +dvclive.init("training_metrics") +model.fit(x_train, + y_train, + validation_data=(x_test, y_test), + batch_size=128, + epochs=3, + callbacks=[MetricsCallback()]) +``` + +DVC provides extensive integration with dvclive. When one is using dvclive in a +project managed by DVC, there is no need for manual initialization of dvclive +inside the code. + +So in case of our code we can remove the following line: + +```python +dvclive.init("training_metrics") +``` + +Now, lets use dvc to run the project: + +```dvc +$ dvc run -n train --live training_metrics -d train.py python train.py +``` + +DVC integration will allow to pass the information that `training_metrics` is +`path` argument for `dvclive.init`. Other supported args for DVC integration: + +- `--live-no-summary` - passes `summary=False` into the `dvclive`. +- `--live-no-html` - passes `html=False` into the `dvclive`. + +> Note that those `dvc run` params are only convinience methods. If you decide +> to invoke `dvclive.init` manually, the manual call config will override +> provided `run` args. In such case your `path` arg for `dvclive.init` must +> match `--live` argument. + +After the training is done you should see following content: + +```bash +$ ls + +dvc.lock training_metrics training_metrics.json +dvc.yaml training_metrics.html train.py +``` + +`training_metrics.json` and `training_metrics.html` are there because we did not +provide `--live-no-sumary` nor `--live-no-html`. If you will open +`training_metrics.html` in your browser, you will get plots for metrics logged +during the training. + +![](/img/dvclive_report.png) + +In `dvc.yaml` there is new stage defined, containing information about the +`dvclive` outputs: + +```bash +$ cat dvc.yaml + +stages: + train: + cmd: python train.py + deps: + - train.py + live: + training_metrics: + summary: true + html: true +``` diff --git a/content/docs/dvclive/usage.md b/content/docs/dvclive/usage.md index d1e92432cc..0c8f3b8bd1 100644 --- a/content/docs/dvclive/usage.md +++ b/content/docs/dvclive/usage.md @@ -5,7 +5,7 @@ training code in order to see how one can introduce `dvclive` into the workflow. In order to run the example, [keras](https://keras.io/about/#installation-amp-compatibility) is required. -The code: +The training code (`train.py` file): ```python from keras.datasets import mnist @@ -51,9 +51,9 @@ model.fit(x_train, epochs=3) ``` -Run the code to verify the training is running. +Run the code to verify the training is executing. -In this example we are training the `model` for 10 epochs. Lets use `dvclive` to +In this example we are training the `model` for 3 epochs. Lets use `dvclive` to log the `accuracy`, `loss`, `validation_accuracy` and `validation_loss` after each epoch, so that we can observe how our training progresses. @@ -63,7 +63,7 @@ In order to do that, we will need to provide proper ```python from keras.callbacks import Callback import dvclive -class DvcLiveCallback(Callback): +class MetricsCallback(Callback): def on_epoch_end(self, epoch: int, logs: dict = None): logs = logs or {} for metric, value in logs.items(): @@ -113,14 +113,6 @@ $ ls training_metrics training_metrics.json train.py ``` -Besides directory, `training_metrics.json` has been created. It's file -containing information about latest training step. You can prevent its creation -during `dvclive.init` call: - -```python -dvclive.init('training_metrics', summary=False) -``` - `training_metrics` directory contains `*.tsv` files with names respective to metrics logged during training: @@ -137,10 +129,32 @@ training_metrics Each of the files contains metric values logged in every training step: ```bash -$ training_metrics/accuracy.tsv +$ cat training_metrics/accuracy.tsv timestamp step accuracy 1614129197192 0 0.7612833380699158 1614129198031 1 0.8736833333969116 1614129198848 2 0.8907166719436646 ``` + +### Configuring dvclive + +Besides `training_metrics `directory, `training_metrics.json` has been created. +It's a file containing information about latest training step. You can prevent +its creation by providing proper `dvclive.init` config flag. + +Args supported by `dvclive.init`: + +- `path` - directory where `dvclive` will write its outputs +- `resume` (`False` by default) - If set to `True`, `dvclive` will try to read + latest `step` from `{path}` dir. Following `next_step` calls will increment + basing on found value. +- `step` (`0`) - If set, the `step` values in logs files will start incrementing + from given value. If provided alongside `resume`, `dvclive` will not try to + find latest `step` in `{path}` and start from `step`. +- `summary` (`True`) - upon each `next_step` call `dvclive` will dump a json + file containing all metrics gathered in last step. The json file has the + following name: `{path}.json`. +- `html` (`True`) - works only when `dvclive` is used alongside DVC. 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