diff --git a/docs/tutorial/deploy.ipynb b/docs/tutorial/deploy.ipynb index 73ea2d9..c239d3a 100644 --- a/docs/tutorial/deploy.ipynb +++ b/docs/tutorial/deploy.ipynb @@ -6,9 +6,9 @@ "source": [ "# Harden and deploy your app\n", "\n", - "Finally, it's time to deploy your model. But before you have to make sure you follow AI deployment [best-practices](https://se-ml.github.io/). In the past, this step was too often either the source of unexpected struggles, or worse, simply ignored.\n", + "Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment [best-practices](https://se-ml.github.io/). In the past, this step was too often either the source of unexpected struggles, or worse, simply ignored.\n", "\n", - "With `GreatAI`, it has become a matter of 2 lines of code." + "With `GreatAI`, it has become a matter of 4 lines of code." ] }, { @@ -20,22 +20,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;226m2022-07-09 22:38:56 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n", - "\u001b[38;5;226m2022-07-09 22:38:56 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n", - "\u001b[38;5;226m2022-07-09 22:38:56 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n", - "\u001b[38;5;226m2022-07-09 22:38:56 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:56 | INFO | GreatAI (v0.1.0): configured ✅\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:56 | INFO | 🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:56 | INFO | 🔩 large_file_implementation: LargeFileLocal\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:56 | INFO | 🔩 is_production: False\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:56 | INFO | 🔩 should_log_exception_stack: True\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:56 | INFO | 🔩 prediction_cache_size: 512\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:56 | INFO | 🔩 dashboard_table_size: 20\u001b[0m\n", - "\u001b[38;5;226m2022-07-09 22:38:56 | WARNING | You still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n", - "\u001b[38;5;226m2022-07-09 22:38:56 | WARNING | > Find out more at https://se-ml.github.io/practices\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:56 | INFO | Fetching cached versions of my-domain-predictor\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:56 | INFO | Latest version of my-domain-predictor is 5 (from versions: 0, 1, 2, 3, 4, 5)\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:56 | INFO | File my-domain-predictor-5 found in cache\u001b[0m\n" + "\u001b[38;5;226mEnvironment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n", + "\u001b[38;5;226mCannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n", + "\u001b[38;5;226mThe selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n", + "\u001b[38;5;226mCannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n", + "\u001b[38;5;39mGreatAI (v0.1.4): configured ✅\u001b[0m\n", + "\u001b[38;5;39m 🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n", + "\u001b[38;5;39m 🔩 large_file_implementation: LargeFileLocal\u001b[0m\n", + "\u001b[38;5;39m 🔩 is_production: False\u001b[0m\n", + "\u001b[38;5;39m 🔩 should_log_exception_stack: True\u001b[0m\n", + "\u001b[38;5;39m 🔩 prediction_cache_size: 512\u001b[0m\n", + "\u001b[38;5;39m 🔩 dashboard_table_size: 50\u001b[0m\n", + "\u001b[38;5;226mYou still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n", + "\u001b[38;5;226m> Find out more at https://se-ml.github.io/practices\u001b[0m\n", + "\u001b[38;5;39mFetching cached versions of my-domain-predictor\u001b[0m\n", + "\u001b[38;5;39mLatest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9)\u001b[0m\n", + "\u001b[38;5;39mFile my-domain-predictor-9 found in cache\u001b[0m\n" ] } ], @@ -59,16 +59,16 @@ { "data": { "text/plain": [ - "Trace[str]({ 'created': '2022-07-09T20:38:56.394746',\n", + "Trace[str]({'created': '2022-07-12T13:34:26.743292',\n", " 'exception': None,\n", " 'feedback': None,\n", " 'logged_values': { 'arg:sentence:length': 29,\n", " 'arg:sentence:value': 'Mountains are just big rocks.'},\n", - " 'models': [{'key': 'my-domain-predictor', 'version': 5}],\n", - " 'original_execution_time_ms': 4.999,\n", + " 'models': [{'key': 'my-domain-predictor', 'version': 9}],\n", + " 'original_execution_time_ms': 6.9699,\n", " 'output': 'geography',\n", " 'tags': ['predict_domain', 'online', 'development'],\n", - " 'trace_id': 'aad1f83d-a81f-4b8b-898e-d02f8076616f'})" + " 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'})" ] }, "execution_count": 2, @@ -77,8 +77,16 @@ } ], "source": [ - "predict_domain(\"Mountains are just big rocks.\")\n", - "# the original return value is under the 'output' key" + "predict_domain(\"Mountains are just big rocks.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the original return value is under the `.output` key. Additionally, a plethora of metadata has been added which will be useful later on.\n", + "\n", + "Running your app in development-mode is as easy as executing `great-ai deploy.ipynb` from your terminal." ] }, { @@ -90,33 +98,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[38;5;39m2022-07-09 22:38:58 | INFO | Converting notebook to Python script\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:58 | INFO | Found `predict_domain` to be the GreatAI app \u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:38:58 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit)\u001b[0m\n", - "\u001b[38;5;226m2022-07-09 22:39:00 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n", - "\u001b[38;5;226m2022-07-09 22:39:00 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n", - "\u001b[38;5;226m2022-07-09 22:39:00 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n", - "\u001b[38;5;226m2022-07-09 22:39:00 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | GreatAI (v0.1.0): configured ✅\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | 🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | 🔩 large_file_implementation: LargeFileLocal\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | 🔩 is_production: False\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | 🔩 should_log_exception_stack: True\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | 🔩 prediction_cache_size: 512\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | 🔩 dashboard_table_size: 20\u001b[0m\n", - "\u001b[38;5;226m2022-07-09 22:39:00 | WARNING | You still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n", - "\u001b[38;5;226m2022-07-09 22:39:00 | WARNING | > Find out more at https://se-ml.github.io/practices\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | Fetching cached versions of my-domain-predictor\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | Latest version of my-domain-predictor is 5 (from versions: 0, 1, 2, 3, 4, 5)\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | File my-domain-predictor-5 found in cache\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | Started server process [882179]\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | Waiting for application startup.\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:00 | INFO | Application startup complete.\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:28 | INFO | Converting notebook to Python script\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app \u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit)\u001b[0m\n", + "\u001b[38;5;226m2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n", + "\u001b[38;5;226m2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n", + "\u001b[38;5;226m2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n", + "\u001b[38;5;226m2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured ✅\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | 🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | 🔩 large_file_implementation: LargeFileLocal\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | 🔩 is_production: False\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | 🔩 should_log_exception_stack: True\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | 🔩 prediction_cache_size: 512\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | 🔩 dashboard_table_size: 50\u001b[0m\n", + "\u001b[38;5;226m2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n", + "\u001b[38;5;226m2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9)\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | Started server process [199794]\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | Waiting for application startup.\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:31 | INFO | Application startup complete.\u001b[0m\n", "^C\n", - "\u001b[38;5;39m2022-07-09 22:39:04 | INFO | Shutting down\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:04 | INFO | Waiting for application shutdown.\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:04 | INFO | Application shutdown complete.\u001b[0m\n", - "\u001b[38;5;39m2022-07-09 22:39:04 | INFO | Finished server process [882179]\u001b[0m\n" + "\u001b[38;5;39m2022-07-12 15:34:33 | INFO | Shutting down\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:33 | INFO | Waiting for application shutdown.\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:33 | INFO | Application shutdown complete.\u001b[0m\n", + "\u001b[38;5;39m2022-07-12 15:34:33 | INFO | Finished server process [199794]\u001b[0m\n" ] } ], @@ -129,16 +137,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "Congrats, you've just created your first GreatAI service! 🎉\n", + "\n", "Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already has an established routine for deploying services.\n", "\n", - "There are three main approaches to deploy a GreatAI service: For more info about them, check out [the deployment how-to](/how-to-guides/use-service)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### [Go back to the summary](/tutorial)" + "There are three main approaches to deploy a GreatAI service: For more info about them, check out [the deployment how-to](/how-to-guides/use-service).\n", + "\n", + "For more thorough examples, see the [examples page](/examples).\n", + "\n", + "### [Go back to the summary](/tutorial/#summary)" ] } ], diff --git a/docs/tutorial/index.md b/docs/tutorial/index.md index d295379..6edb8d1 100644 --- a/docs/tutorial/index.md +++ b/docs/tutorial/index.md @@ -1,11 +1,11 @@ # Train and deploy a SOTA model -Let's see GreatAI in action by going over the life-cycle of a simple service. +Let's see `great-ai` in action by going over the life-cycle of a simple service. ## Objectives -1. You will see how the [great_ai.utilities][] can integrate into your Data Science workflow. -2. You will use [great_ai.large_file][] to version and store your trained model. +1. You will see how the [great_ai.utilities](/reference/utilities) can integrate into your Data Science workflow. +2. You will use [great_ai.large_file](/reference/large_file) to version and store your trained model. 3. You will use [GreatAI][great_ai.GreatAI] to prepare your model for a robust and responsible deployment. ## Overview @@ -14,7 +14,6 @@ You are going to train a field of study (domain) classifier for scientific sente We use the same synthetic dataset derived from the [Microsoft Academic Graph](https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/). The dataset is [available here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag). - !!! success You are ready to start the tutorial. Feel free to come back to the [summary](#summary) section once you're finished. @@ -24,19 +23,18 @@ We use the same synthetic dataset derived from the [Microsoft Academic Graph](ht [:material-cloud-tags: Deploy it](deploy.ipynb){ .md-button .md-button--primary } - ## Summary ### The [training notebook](train.ipynb) -We load and preprocess the dataset while relying on [great_ai.utilities.clean][] for the heavy-lifting. Additionally, the preprocessing is parallelised using [great_ai.utilities.simple_parallel_map][] +We load and preprocess the dataset while relying on [great_ai.utilities.clean][great_ai.utilities.clean.clean] for doing the heavy-lifting. Additionally, the preprocessing is parallelised using [great_ai.utilities.simple_parallel_map][] After training and evaluating a model, it is exported using [great_ai.save_model][]. ??? tip "Remote storage" To store your model remotely, you need to set your credentials before calling `save_model`. - For example, to use [AWS S3](https://aws.amazon.com/s3/): + For example, to use [AWS S3](https://aws.amazon.com/s3){ target=_blank }: ```python from great_ai.large_file import LargeFileS3 @@ -52,6 +50,8 @@ After training and evaluating a model, it is exported using [great_ai.save_model save_model(model, key='my-domain-predictor') ``` + For more info, checkout [the configuration how-to page](/how-to-guides/configure-service). + ### The [deployment notebook](deploy.ipynb) We create an inference function that can be hardened by wrapping it in a [GreatAI][great_ai.GreatAI] instance. @@ -68,6 +68,10 @@ def predict_domain(sentence, model): ``` 1. [@use_model][great_ai.use_model] loads and injects your model into the `predict_domain` function's `model` argument. - You can freely reference it knowing that it is always given to the function. + You can freely reference it knowing that the function is always provided with it. -Finally, we test the model's inference function through the GreatAI dashboard. [The only thing left is to deploy the hardened-service.](/how-to-guides/use-service) \ No newline at end of file +Finally, we test the model's inference function through the GreatAI dashboard. [The only thing left is to deploy the hardened-service properly.](/how-to-guides/use-service) + +
+[:material-book: Learn about more features](/how-to-guides/create-service){ .md-button .md-button--primary } +
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"Requirement already satisfied: tornado>=6.0 in /data/projects/great-ai/.env/lib/python3.10/site-packages (from jupyter-client>=6.1.5->nbclient>=0.5.0->nbconvert->great-ai) (6.2)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ - "%pip install great-ai" + "%pip install great-ai > /dev/null" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "First, we have to get some data. After downloading it [from here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag), we might notice that the dataset is in [JSON Lines](https://jsonlines.org/) format (each line is a seperate JSON document). \n", "\n", - "First, we have to get some data. After downloading it [from here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag), we might notice that the dataset is in [JSON Lines](https://jsonlines.org/) format (each line is a seperate JSON). \n", - "\n", - "Let's write a function which takes a single line, and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts." + "Let's write a function which takes a single line, and returns the sentence and the corresponding label from it. Before returning, the sentence is also [cleaned](/reference/utilities/#great_ai.utilities.clean.clean) to remove any LaTeX, XML, unicode, PDF-extraction artifacts." ] }, { @@ -168,7 +58,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we can load the dataset and extract the *training* samples from it. Since we're impatient, we can do it in parallel using the `simple_parallel_map` function." + "Now, we can load the dataset and extract the *training* samples from it. Since we're impatient, we can do it in parallel using the [`simple_parallel_map`](/reference/utilities/#great_ai.utilities.simple_parallel_map) function.\n", + "\n", + "> Open files in Python are iterable, with a line being return (in text mode) with each iteration." ] }, { @@ -180,7 +72,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 84000/84000 [00:08<00:00, 9771.59it/s] \n" + "100%|██████████| 84000/84000 [00:09<00:00, 8960.63it/s] \n" ] } ], @@ -188,7 +80,7 @@ "from great_ai.utilities import simple_parallel_map\n", "\n", "with open(\"data/train.txt\", encoding=\"utf-8\") as f:\n", - " training_data = simple_parallel_map(preprocess, f.readlines())\n", + " training_data = simple_parallel_map(preprocess, f)\n", "\n", "X_train = [d[0] for d in training_data]\n", "y_train = [d[1] for d in training_data]" @@ -210,13 +102,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 22399/22399 [00:02<00:00, 7858.71it/s] \n" + "100%|██████████| 22399/22399 [00:03<00:00, 6078.02it/s]\n" ] } ], "source": [ "with open(\"data/test.txt\", encoding=\"utf-8\") as f:\n", - " test_data = simple_parallel_map(preprocess, f.readlines())\n", + " test_data = simple_parallel_map(preprocess, f)\n", "\n", "X_test = [d[0] for d in test_data]\n", "y_test = [d[1] for d in test_data]" @@ -267,7 +159,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. \n", + "It's already finished. Let's see how well it performs. But first, we need to [configure matplotlib](https://matplotlib.org/stable/tutorials/introductory/customizing.html) to make the charts more legible. \n", "> `ConfusionMatrixDisplay` relies on `matplotlib` for rendering." ] }, @@ -281,6 +173,7 @@ "\n", "%matplotlib inline\n", "plt.rcParams[\"figure.figsize\"] = (30, 15)\n", + "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", "plt.rcParams[\"font.size\"] = 12" ] }, @@ -318,14 +211,12 @@ }, { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -345,11 +236,11 @@ "source": [ "Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about **5% better than SciBERT's** 0.6571!\n", "\n", - "You might wonder that \"this is great, but besides some utility functions (`clean`, `simple_parallel_map`, ...) what more value does GreatAI add?\". This would be a valid argument because the scope of GreatAI actually only starts here.\n", + "You might wonder that *\"this is great, but besides some utility functions (`clean`, `simple_parallel_map`, ...) what more value does GreatAI add?\"*. This would be a valid argument because the scope of GreatAI actually only starts here.\n", "\n", "> Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey-zone for software engineering.\n", "\n", - "In order to use this model in production, we have to make it accessible on some possibly shared infrastructure." + "In order to use this model in production, we have to make it available on some possibly shared infrastructure." ] }, { @@ -365,7 +256,7 @@ "\u001b[38;5;226mCannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n", "\u001b[38;5;226mThe selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n", "\u001b[38;5;226mCannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n", - "\u001b[38;5;39mGreatAI (v0.1.3): configured ✅\u001b[0m\n", + "\u001b[38;5;39mGreatAI (v0.1.4): configured ✅\u001b[0m\n", "\u001b[38;5;39m 🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n", "\u001b[38;5;39m 🔩 large_file_implementation: LargeFileLocal\u001b[0m\n", "\u001b[38;5;39m 🔩 is_production: False\u001b[0m\n", @@ -375,15 +266,15 @@ "\u001b[38;5;226mYou still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n", "\u001b[38;5;226m> Find out more at https://se-ml.github.io/practices\u001b[0m\n", "\u001b[38;5;39mFetching cached versions of my-domain-predictor\u001b[0m\n", - "\u001b[38;5;39mCopying file for my-domain-predictor-6\u001b[0m\n", - "\u001b[38;5;39mCompressing my-domain-predictor-6\u001b[0m\n", - "\u001b[38;5;39mModel my-domain-predictor uploaded with version 6\u001b[0m\n" + "\u001b[38;5;39mCopying file for my-domain-predictor-9\u001b[0m\n", + "\u001b[38;5;39mCompressing my-domain-predictor-9\u001b[0m\n", + "\u001b[38;5;39mModel my-domain-predictor uploaded with version 9\u001b[0m\n" ] }, { "data": { "text/plain": [ - "'my-domain-predictor:6'" + "'my-domain-predictor:9'" ] }, "execution_count": 8,