Hey, I know there is this tflite runtime version specified in https://www.kaggle.com/competitions/asl-fingerspelling/discussion/414682 by @sohier . I am able to save my model and evaluate the same in that tflite runtime version but it errors out in submission. I know that it may be erroring out due to edge cases but I have handled the same as others have done. I am inching closer to a valid submission by performing ablation on my model but its going to take a lot of iteration. I am not sure about saving and evaluating with specified tflite runtime suffice to say that my model is compatible and something else might be the problem.
\nMy point being, it would be great to have a environment in which I can be sure that my model is compatible with submission environment and it does not error out because of tflite runtime incompatibility. It would be very much help with iteration process for just a valid submission.
", "messages": [ { "id": 2322390, "postDate": "2023-06-29T08:54:14.053Z", "content": "for just a valid submission.
\nI understand your concerns and the need for a compatible environment to ensure smooth submissions. While I can't provide a specific environment for the ASL Fingerspelling competition, I can suggest a general approach to create a custom environment that is as close to the submission environment as possible.
\nCreate a virtual environment: This will help isolate the dependencies from your system's global Python environment. You can use virtualenv or conda to create a virtual environment. For example, with virtualenv:
\npip install virtualenv\nvirtualenv asl_env\nsource asl_env/bin/\n\n\nOr `conda`:\n\nconda create \nconda asl_env\nInstall the specified TensorFlow Lite runtime version: Make sure to install the exact version mentioned in the competition discussion. For instance, if the specified version is 2.5.0, you would install it like this:
\npip install --index-url https:oogle-coral.github.io tflite_runtime==.\nInstall other necessary libraries: Install any required libraries to load, process, and evaluate your model. For example:
\npip numpy\npip opencv-python\nTest your model: Write a script that loads your TFLite model, processes the input data, and evaluates the model in the same way it would be evaluated during submission. This will help you identify and fix any incompatibilities or edge cases that might cause issues during submission.
\nKeep track of your environment: To ensure reproducibility, you can generate a requirements.txt file that lists all dependencies and their versions:
\npgsql\n\npip > requirements.txt\nYou can also share this file with others so that they can easily recreate the same environment.
\nBy following these steps, you should be able to create an environment that closely resembles the submission environment, allowing you to test your model and minimize errors during submission. Good luck with the competition!
I know virtual environments are helpful. Its the runtime version I am more concerned with. Fortunately, recently updated evaluation page specifies some versions of core libraries and it should be a good starting point.
", "rawMarkdown": "I know virtual environments are helpful. Its the runtime version I am more concerned with. Fortunately, recently updated evaluation page specifies some versions of core libraries and it should be a good starting point." } ] }, { "id": 2322044, "postDate": "2023-06-29T03:52:46.543Z", "content": "Hey, I know there is this tflite runtime version specified in https://www.kaggle.com/competitions/asl-fingerspelling/discussion/414682 by @sohier . I am able to save my model and evaluate the same in that tflite runtime version but it errors out in submission. I know that it may be erroring out due to edge cases but I have handled the same as others have done. I am inching closer to a valid submission by performing ablation on my model but its going to take a lot of iteration. I am not sure about saving and evaluating with specified tflite runtime suffice to say that my model is compatible and something else might be the problem.
\nMy point being, it would be great to have a environment in which I can be sure that my model is compatible with submission environment and it does not error out because of tflite runtime incompatibility. It would be very much help with iteration process for just a valid submission.
", "rawMarkdown": "Hey, I know there is this tflite runtime version specified in https://www.kaggle.com/competitions/asl-fingerspelling/discussion/414682 by @sohier . I am able to save my model and evaluate the same in that tflite runtime version but it errors out in submission. I know that it may be erroring out due to edge cases but I have handled the same as others have done. I am inching closer to a valid submission by performing ablation on my model but its going to take a lot of iteration. I am not sure about saving and evaluating with specified tflite runtime suffice to say that my model is compatible and something else might be the problem.\n\nMy point being, it would be great to have a environment in which I can be sure that my model is compatible with submission environment and it does not error out because of tflite runtime incompatibility. It would be very much help with iteration process for just a valid submission.", "votes": 2 }, { "id": 2323501, "postDate": "2023-06-30T02:18:28.797Z", "content": "Recently updated instructions at https://www.kaggle.com/competitions/asl-fingerspelling/overview/evaluation should help.
", "rawMarkdown": "Recently updated instructions at https://www.kaggle.com/competitions/asl-fingerspelling/overview/evaluation should help. " } ], "comments": [ { "id": 2322390, "author_name": "Shouxiang", "author_url": "", "post_date": "2023-06-29T08:54:14.053000", "content": "for just a valid submission.
\nI understand your concerns and the need for a compatible environment to ensure smooth submissions. While I can't provide a specific environment for the ASL Fingerspelling competition, I can suggest a general approach to create a custom environment that is as close to the submission environment as possible.
\nCreate a virtual environment: This will help isolate the dependencies from your system's global Python environment. You can use virtualenv or conda to create a virtual environment. For example, with virtualenv:
\npip install virtualenv\nvirtualenv asl_env\nsource asl_env/bin/\n\n\nOr `conda`:\n\nconda create \nconda asl_env\nInstall the specified TensorFlow Lite runtime version: Make sure to install the exact version mentioned in the competition discussion. For instance, if the specified version is 2.5.0, you would install it like this:
\npip install --index-url https:oogle-coral.github.io tflite_runtime==.\nInstall other necessary libraries: Install any required libraries to load, process, and evaluate your model. For example:
\npip numpy\npip opencv-python\nTest your model: Write a script that loads your TFLite model, processes the input data, and evaluates the model in the same way it would be evaluated during submission. This will help you identify and fix any incompatibilities or edge cases that might cause issues during submission.
\nKeep track of your environment: To ensure reproducibility, you can generate a requirements.txt file that lists all dependencies and their versions:
\npgsql\n\npip > requirements.txt\nYou can also share this file with others so that they can easily recreate the same environment.
\nBy following these steps, you should be able to create an environment that closely resembles the submission environment, allowing you to test your model and minimize errors during submission. Good luck with the competition!
I know virtual environments are helpful. Its the runtime version I am more concerned with. Fortunately, recently updated evaluation page specifies some versions of core libraries and it should be a good starting point.
", "votes": 0, "replies": [] } ] }, { "id": 2323501, "author_name": "Knightwing", "author_url": "", "post_date": "2023-06-30T02:18:28.797000", "content": "Recently updated instructions at https://www.kaggle.com/competitions/asl-fingerspelling/overview/evaluation should help.
", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "2322390": "for just a valid submission.\n\nI understand your concerns and the need for a compatible environment to ensure smooth submissions. While I can't provide a specific environment for the ASL Fingerspelling competition, I can suggest a general approach to create a custom environment that is as close to the submission environment as possible.\n\nCreate a virtual environment: This will help isolate the dependencies from your system's global Python environment. You can use virtualenv or conda to create a virtual environment. For example, with virtualenv:\n\n```\npip install virtualenv\nvirtualenv asl_env\nsource asl_env/bin/activate\n\n\nOr with `conda`:\n\nconda create --name asl_env python=3.7\nconda activate asl_env\n\n```\nInstall the specified TensorFlow Lite runtime version: Make sure to install the exact version mentioned in the competition discussion. For instance, if the specified version is 2.5.0, you would install it like this:\n\n```\npip install --index-url https://google-coral.github.io/py-repo/ tflite_runtime==2.5.0\n```\n\nInstall other necessary libraries: Install any required libraries to load, process, and evaluate your model. For example:\n\n```\npip install numpy\npip install opencv-python\n```\n\nTest your model: Write a script that loads your TFLite model, processes the input data, and evaluates the model in the same way it would be evaluated during submission. This will help you identify and fix any incompatibilities or edge cases that might cause issues during submission.\n\nKeep track of your environment: To ensure reproducibility, you can generate a requirements.txt file that lists all dependencies and their versions:\n```\npgsql\nCopy\npip freeze > requirements.txt\n```\n\nYou can also share this file with others so that they can easily recreate the same environment.\nBy following these steps, you should be able to create an environment that closely resembles the submission environment, allowing you to test your model and minimize errors during submission. Good luck with the competition!", "2322044": "Hey, I know there is this tflite runtime version specified in https://www.kaggle.com/competitions/asl-fingerspelling/discussion/414682 by @sohier . I am able to save my model and evaluate the same in that tflite runtime version but it errors out in submission. I know that it may be erroring out due to edge cases but I have handled the same as others have done. I am inching closer to a valid submission by performing ablation on my model but its going to take a lot of iteration. I am not sure about saving and evaluating with specified tflite runtime suffice to say that my model is compatible and something else might be the problem.\n\nMy point being, it would be great to have a environment in which I can be sure that my model is compatible with submission environment and it does not error out because of tflite runtime incompatibility. It would be very much help with iteration process for just a valid submission.", "2323501": "Recently updated instructions at https://www.kaggle.com/competitions/asl-fingerspelling/overview/evaluation should help. " } }