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experiemnt logs
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‎docs/experiments/nlp_autoregression.html

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‎docs/experiments/nlp_classification.html

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‎docs/normalization/deep_norm/experiment.html

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‎docs/normalization/deep_norm/index.html

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‎docs/sitemap.xml

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@@ -190,14 +190,14 @@
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<url>
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<loc>https://nn.labml.ai/normalization/deep_norm/index.html</loc>
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<lastmod>2022-04-10T16:30:00+00:00</lastmod>
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<lastmod>2022-04-23T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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<url>
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<loc>https://nn.labml.ai/normalization/deep_norm/experiment.html</loc>
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<lastmod>2022-04-10T16:30:00+00:00</lastmod>
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<lastmod>2022-04-23T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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@@ -260,7 +260,7 @@
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<url>
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<loc>https://nn.labml.ai/resnet/index.html</loc>
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<lastmod>2021-10-21T16:30:00+00:00</lastmod>
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<lastmod>2022-04-10T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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‎labml_nn/experiments/nlp_autoregression.py

Lines changed: 12 additions & 2 deletions
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@@ -91,6 +91,16 @@ class NLPAutoRegressionConfigs(TrainValidConfigs):
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# Data loaders shuffle with replacement
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dataloader_shuffle_with_replacement: bool = False
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# Whether to log model parameters and gradients (once per epoch).
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# These are summarized stats per layer, but it could still lead
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# to many indicators for very deep networks.
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is_log_model_params_grads: bool = False
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# Whether to log model activations (once per epoch).
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# These are summarized stats per layer, but it could still lead
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# to many indicators for very deep networks.
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is_log_model_activations: bool = False
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def init(self):
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"""
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### Initialization
@@ -126,7 +136,7 @@ def step(self, batch: any, batch_idx: BatchIndex):
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tracker.add_global_step(data.shape[0] * data.shape[1])
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# Whether to capture model outputs
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with self.mode.update(is_log_activations=batch_idx.is_last):
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with self.mode.update(is_log_activations=batch_idx.is_last and self.is_log_model_activations):
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# Get model outputs.
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# It's returning a tuple for states when using RNNs.
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# This is not implemented yet. 😜
@@ -151,7 +161,7 @@ def step(self, batch: any, batch_idx: BatchIndex):
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# Take optimizer step
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self.optimizer.step()
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# Log the model parameters and gradients on last batch of every epoch
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if batch_idx.is_last:
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if batch_idx.is_last and self.is_log_model_params_grads:
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tracker.add('model', self.model)
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# Clear the gradients
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self.optimizer.zero_grad()

‎labml_nn/experiments/nlp_classification.py

Lines changed: 12 additions & 2 deletions
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@@ -74,6 +74,16 @@ class NLPClassificationConfigs(TrainValidConfigs):
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# Validation data loader
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valid_loader: DataLoader = 'ag_news'
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# Whether to log model parameters and gradients (once per epoch).
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# These are summarized stats per layer, but it could still lead
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# to many indicators for very deep networks.
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is_log_model_params_grads: bool = False
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# Whether to log model activations (once per epoch).
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# These are summarized stats per layer, but it could still lead
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# to many indicators for very deep networks.
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is_log_model_activations: bool = False
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def init(self):
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"""
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### Initialization
@@ -102,7 +112,7 @@ def step(self, batch: any, batch_idx: BatchIndex):
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tracker.add_global_step(data.shape[1])
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# Whether to capture model outputs
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with self.mode.update(is_log_activations=batch_idx.is_last):
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with self.mode.update(is_log_activations=batch_idx.is_last and self.is_log_model_activations):
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# Get model outputs.
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# It's returning a tuple for states when using RNNs.
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# This is not implemented yet. 😜
@@ -125,7 +135,7 @@ def step(self, batch: any, batch_idx: BatchIndex):
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# Take optimizer step
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self.optimizer.step()
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# Log the model parameters and gradients on last batch of every epoch
128-
if batch_idx.is_last:
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if batch_idx.is_last and self.is_log_model_params_grads:
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tracker.add('model', self.model)
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# Clear the gradients
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self.optimizer.zero_grad()

‎labml_nn/transformers/basic/autoregressive_experiment.ipynb

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@@ -17,7 +17,10 @@
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AYV_dMVDxyc2"
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"id": "AYV_dMVDxyc2",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"[![Github](https://img.shields.io/github/stars/labmlai/annotated_deep_learning_paper_implementations?style=social)](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n",
@@ -34,7 +37,10 @@
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AahG_i2y5tY9"
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"id": "AahG_i2y5tY9",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"Install the packages"
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "cf107fb2-4d50-4c67-af34-367624553421"
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"outputId": "cf107fb2-4d50-4c67-af34-367624553421",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"source": [
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"!pip install labml-nn comet_ml"
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "SE2VUQ6L5zxI"
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"id": "SE2VUQ6L5zxI",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"Imports"
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{
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"cell_type": "code",
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"metadata": {
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"id": "0hJXx_g0wS2C"
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"id": "0hJXx_g0wS2C",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"source": [
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"import torch\n",
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Lpggo0wM6qb-"
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"id": "Lpggo0wM6qb-",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"Create an experiment"
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{
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"cell_type": "code",
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"metadata": {
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"id": "bFcr9k-l4cAg"
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"id": "bFcr9k-l4cAg",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"source": [
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"experiment.create(name=\"transformer\", writers={'screen', 'web_api', 'comet'})"
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "-OnHLi626tJt"
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"id": "-OnHLi626tJt",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"Initialize configurations"
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{
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"cell_type": "code",
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"metadata": {
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"id": "Piz0c5f44hRo"
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"id": "Piz0c5f44hRo",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"source": [
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"conf = Configs()"
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "wwMzCqpD6vkL"
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"id": "wwMzCqpD6vkL",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"Set experiment configurations and assign a configurations dictionary to override configurations"
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"height": 17
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},
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"id": "e6hmQhTw4nks",
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"outputId": "29634715-42f4-4405-fb11-fc9522608627"
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"outputId": "29634715-42f4-4405-fb11-fc9522608627",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"source": [
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"experiment.configs(conf, {\n",
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "EvI7MtgJ61w5"
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"id": "EvI7MtgJ61w5",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"Set PyTorch models for loading and saving"
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"height": 255
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},
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"id": "GDlt7dp-5ALt",
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"outputId": "e7548e8f-c541-4618-dc5a-1597cae42003"
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"outputId": "e7548e8f-c541-4618-dc5a-1597cae42003",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"source": [
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"experiment.add_pytorch_models({'model': conf.model})"
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "KJZRf8527GxL"
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"id": "KJZRf8527GxL",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"Start the experiment and run the training loop."
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"height": 1000
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},
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"id": "aIAWo7Fw5DR8",
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"outputId": "db979785-bfe3-4eda-d3eb-8ccbe61053e5"
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"outputId": "db979785-bfe3-4eda-d3eb-8ccbe61053e5",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"source": [
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"# Start the experiment\n",
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{
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"cell_type": "code",
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"metadata": {
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"id": "oBXXlP2b7XZO"
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"id": "oBXXlP2b7XZO",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"source": [
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""
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],
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"source": [],
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"execution_count": null,
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"outputs": []
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}

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